As a business leader, you should cultivate trusting relationships with your data teams. Doing so is crucial, as implementing data management initiatives and developing different data models may involve input from various stakeholders, including CEOs.

If you develop or implement new software solutions without ensuring that your business objectives align with data management strategies, you won’t be able to succeed in your industry and ensure tangible results for your organization, such as increased profits and higher customer retention and attraction.

In this blog post, you will learn about: 

  • the role data modeling plays in designing a scalable and reliable data architecture for your software system
  • what business benefits you can count on if you follow a tried-and-true data modeling flow
  • common data modeling examples and techniques
  • tools you can use to speed up data modeling
  • how Yalantis can help you with efficient data modeling

Explore time-tested data modeling best practices from Yalantis.

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    What is a data model and why is it important?

    A data model is an easy-to-read graph that considers your business requirements and prepares the ground for designing the software application database. The data modeling process:

    • involves drafting schematic relationships between core business processes and entities that will be the essence of a software application, such as drivers, a vehicle fleet, and orders in a supply chain management software solution
    • begins at the requirements analysis stage prior to software architecture development

    However, the value of data modeling is twofold. The more accurately you approach data modeling of a single application, the better you’ll be able to establish strong data management practices in your organization, as data modeling can help you better understand the condition of your cross-functional data assets. According to Piethein Strengholt, author of the book Data Management at Scale, data modeling is a crucial area of data management.

    Below, you can see the data management pyramid created by Peter Aiken, author of the book Data Management Body of Knowledge. It reflects the typical hierarchy of data management practices in an organization.

    Data governance, which includes policies and procedures for maintaining data integrity within the company, is at the base of the pyramid. The second level is about data architecture, data quality, and metadata management, which lay the foundation for the third level, which is devoted to data security, data storage, and data modeling. In turn, these directly affect data integration and interoperability capabilities, without which such important business initiatives as data analytics and business intelligence won’t function properly.

    Later, we cover how you can build a data model and what stages this process involves to help you dive even deeper into data modeling specifics.

    3 reasons why C-level executives should be actively involved in data modeling

    Here are a few reasons for you to engage in the data modeling process with your data teams:

    1. Ensure that business processes within your data model align with your current and future goals. This is critical to avoid situations where, for example, the C-suite plans a complete restructuring of your finance department and the data model for the organization-wide application doesn’t account for this. Plus, if you participate in data modeling, you can ensure your application is scalable enough to fit your business expansion strategy and avoid costly changes during software development.
    2. Better understand data analytics results and insights. If you’re involved in the data modeling process at the initial stages of software development, you’ll have a more in-depth understanding of what data aspects and relationships are behind the results of your data analytics tools.
    3. Efficiently allocate resources and budget to data management practices. If you’re actively involved in every aspect of data management at your organization, including data modeling, you’ll have a clearer perception of how to reasonably allocate resources to foster data initiatives and increase the value of data at your company.

    Developing software or managing data without a data model is similar to renovating an apartment without a detailed and accurate design. When remodeling, blueprints indicate where all the outlets will be, for instance, so you have enough outlets in the right places and you don’t have to make costly changes after all the work is done. By the same principle, an efficient and well-thought-out data model identifies all important data assets, establishes clear relationships between them, and helps you minimize costly changes during software development.

    To better grasp how data modeling works, let’s cover what challenges you can address with a well-defined data model.

    Prepare for streamlined software development with a well-structured data model.

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    5 benefits of an efficient data model

    Data modeling benefits vary depending on the organization’s size and industry. As we suggested in the title of this article, data modeling is your go-to for developing scalable and secure applications. At the data modeling stage, with the help of diverse tools and best practices, you can:

    • lay the foundation for incremental growth of the data your future application can process while maintaining stable performance 
    • ensure that your data assets don’t corrupt each other and can be safely integrated with other applications integration with other applications is safe

    However, the benefits of data modeling don’t stop at improved security and scalability. We can also speak of the following data modeling advantages for your business:

    Enhanced data management and data governance. Data modeling allows organizations to optimally structure the data their software systems produce. A structured data model is the ultimate cure for chaotic and ill-organized data management.

    Even though a data model can’t solve every problem, it still helps you be more aware of how your key business entities relate to each other and quickly find out if there are departments or processes in your organization that don’t properly structure the data they use and store. 

    Awareness of your data assets and their relationship helps you boost your data governance initiatives to maintain proper data integrity, quality, and usability within your organization. As a consequence, well-defined data use allows your employees and stakeholders to derive tangible value from various data assets.

    Prompt recognition of data issues. Data modeling allows data architects and teams providing data engineering services to accurately define issues that can potentially complicate or disrupt the software development process, such as inconsistent, inaccurate, duplicate, or low-quality data. This data modeling benefit is especially crucial if a software application needs to be integrated with other corporate systems or third-party solutions and it’s important to ensure that data exchange between the solutions is accurate, timely, and seamless.

    Accelerated time to market for your software solutions. With the help of a well-defined data model, a development team can have a clear understanding of the components that need to be built at each application development stage without frequently referring to business stakeholders. 

    Developing a detailed data model may take time as well, but the more prepared you are at the beginning, the quicker your product will be launched (and the more likely you are to avoid cost- and time-intensive workarounds during the software development process). After investing time and resources in data modeling, First Tech Credit Union Bank realized that building a robust data model up front helped reduce the project go-live time and allowed team members to create better business intelligence (BI) reports. 

    Improved project documentation and knowledge sharing. Data modeling requires active cooperation between business and technical stakeholders, ensuring that everyone is on the same page. Such alignment at the start of a project helps you to develop detailed and accurate project documentation to pass the knowledge to other company teams if necessary or use it as the basis for accelerated development of other software systems.

    Preparation for data analytics and visualization. Data modeling is part of business intelligence services and advanced analytics solutions, as it allows business stakeholders to reach a mutual understanding of how data at their organization is organized. And the more accurate the data modeling process, the better and more business-oriented the data analytics. When you have a clear understanding of the relationships between your core data assets, you can set the right criteria for business intelligence and advanced analytics tools to generate beneficial insights.

    Now that you have a general idea of what data modeling is and how you can benefit from its development, let’s move to learning more about different types of data models.

    Learn how we implemented a streamlined data aggregation strategy for a healthcare solution.

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    Types of data models: The simplest is always the most important

    There are three data model types: conceptual, logical, and physical. These are called types of data models, but they are also considered stages of data model creation.

    Conceptual data model example 

    This data modeling type is frequently overlooked and considered insignificant due to its simplistic nature. But conceptual models are exactly what set the tone for the entire data modeling process. Developing conceptual data models involves data architects and critical business stakeholders providing high-level business concepts that describe their business processes, relationships between those processes, and the main entities included in them.

    For a conceptual data modeling example, let’s take an enterprise that needs to develop an application for their manufacturing department and starts by outlining key concepts for the application’s data model. The concepts could be material, product, bill of materials (BOM), assembly line, employee, supplier, and purchase order. There could be more entities depending on the type of applications, objects, and user roles involved. After defining key entities, the next step is establishing relationships between them.

    Logical data model example

    Developing a logical model begins with constructing an entity relationship diagram (ERD) that specifies the relationship between entities in the conceptual model in greater detail. Data in a logical data model can already be organized into tables, columns, and rows. Relationships between entities in the logical data model can fall within the following categories:

    • Many-to-many, when multiple elements in one data table refer to many elements in another table. For example, many teams can work on diverse projects, and projects can involve many team members.
    • One-to-many, when there is one parent element connected to many child elements, such as one customer who can place several orders.
    • One-to-one, when one data element is linked to only one element in another table. For instance, each product can have only one BOM document. 

    It may seem that thinking through all possible relationships between your data entities is a relatively easy task, but it often takes lots of time to unveil all possible and hidden dependencies. If you want to create a software system that engages two or more departments (such as manufacturing and supply chain), you’ll have to work with many more entities.

    And this is when it can get difficult and confusing, especially if certain entities exist across departments. For example, both the manufacturing and supply chain departments may have an order business entity, but it can have a different meaning for each department. For manufacturing, it may refer to an order placed with a supplier company to deliver materials, while for the supply chain department, it may refer to an order placed by a customer.

    Physical data model example

    A physical data model more closely resembles your application’s database than previous models do, and it’s better not to to avoid building it.

    A physical data model defines all columns, tables, relationships, and dependencies between datasets — as well as the context for recording and storing data — allowing data architects to design a complete database for the application. Mainly, a physical model answers the question: How do you implement a database tier in the application architecture?

    Creating conceptual, logical, and physical models is only the beginning of your data modeling journey. In the next section, we’ll learn what’s next.

    Data modeling techniques, or how to ensure smart data organization

    Once a physical data model is created, it’s up to the data architects to combine those tables and columns in a way that corresponds to your business requirements and supports your business operations. Here are a few typical data modeling approaches.


    Relational data modeling

    In a relational model, data is organized in tables, columns, and rows for efficient and simple retrieval. This type of model fits applications with complex many-to-many relationships. Relational data modeling is the most common approach, as it reveals how entities in one table — such as all data about a customer (customer ID, address, state) — relate to data in another table (such as the orders table).

    This model uses a range of keys to organize data. Primary keys (PK) indicate what entities one table includes, and foreign keys (FK) are used to show relations between two separate tables.

    Such common systems as sales management software, supply chain management systems, hospital management systems (HMSs), content management systems (CMSs), and customer relationship management (CRM) systems use a relational data modeling approach for efficient data organization.


    Hierarchical data modeling

    As the name suggests, hierarchical data modeling allows data architects to organize data entities with a parent–child relationship. In such a data model, child nodes can also become parent nodes for their child nodes, but there is always one primary parent node for each set of child nodes. For instance, this type of data modeling suits applications that need to include complex organizational structures, such as all company departments with the board of directors as the entity responsible for all of them. The hierarchical data modeling approach also fits when building file systems with strict subordination.

    Hierarchical data models are considered rather inflexible and have a more difficult procedure for data retrieval than relational data models. If you want a more flexible approach, you can choose a relational model.

    Network data modeling

    The third data modeling approach we’re going to cover is network data modeling, which is an improvement to rigid hierarchical modeling. Network data modeling allows each child branch to have multiple parent nodes. Thus, a one-to-many relationship in a hierarchical model evolves into a many-to-many relationship in a network model.

    This type of data model is a high-level graphical representation that consists of boxes and arrows instead of a complex system of tables and columns as in a relational data model. Boxes indicate data records and arrows relationships between them. 

    The network data modeling technique is suitable for building a complex organizational structure, indicating more relationships between entities than a hierarchical data model. Such models are simple in nature and easy to read, as they look like diagrams but still lack the flexibility of relational data models.

    Object-oriented data modeling 

    This data modeling approach is based on the principles of object-oriented programming and works especially well with programming languages such as C++, Java, and Python. However, the major downside is that this data modeling technique isn’t equally productive when combined with other programming languages. 

    The critical principle of object-oriented data modeling is that instead of defining entities and their unique properties as with relational data models, data architects first define entities that have common properties. Data and real-world actions that can be performed with it (methods) are stored in a single entity, known as an object

    The aim of the object-oriented data modeling approach is to save development resources by making a concise data model and reusing its components whenever possible. Object-oriented modeling can be used in many scenarios, but this approach is particularly beneficial if your application needs to be developed in an object-oriented programming language with a focus on a code-first software development approach (when application code to implement functionality is developed prior to the software architecture and other documentation).

    In this section, we’ve covered the most typical data modeling techniques, but there are many more that spring up as the result of evolving digital technologies. Plus, there’s the possibility of building hybrid data models that combine several techniques. Which data model is best for your project depends on the unique functional and non-functional requirements you’ve defined for your software system. 

    Let’s move on to software solutions that can facilitate the data modeling process.

    Get step-by-step guidance on how to develop an enterprise data warehouse with data model examples.

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    Tools and software that foster data modeling

    The software development market boasts lots of data modeling software and tools that simplify this process, making it more efficient. We’ll discuss five common solutions.

    Lucidchart is a visualization tool suited for building data models and software architecture. It is easy to understand and supports shared real-time collaboration for development teams. Most of its features are available for free, and Lucidchart is a perfect tool for quick data modeling that’s understandable for technical and non-technical stakeholders.

    Erwin Data Modeler is the top pick for many companies across diverse industries, as it also includes metadata management, data governance, and data intelligence. In fact, Erwin Data Modeler boasts the widest range of features compared to other tools we list here. Users can easily customize the data modeling canvas to their needs and use only those functionalities that suit them.

    ER/Studio boasts an extremely convenient collaborative environment and provides lots of scalable opportunities for enterprises of all sizes. ER/Studio offers efficient functionality for data architects such as forward and reverse engineering. Thus, this tool allows for end-to-end database modeling along with creating a data architecture. ER/Studio also has a wide variety of free tools that help you simplify data modeling without making any financial investment.

    Toad Data Modeler is another convenient tool for data architects, as apart from standard data modeling features, it offers separate solutions to maximize integration with various databases. Toad Modeler also allows end users to generate extensive reports based on data models or data architectures.

    You should discuss types of data modeling solutions with your technical teams to discover the service that best fits your needs, your business model, and the skills of your data architects and database administrators.

    If you have a software solution in mind and are unsure where to start, composing a data model can be the perfect way to structure and visualize your abstract ideas and assess their feasibility. Yalantis’ expert team can choose fitting data modeling tools, apply data modeling best practices, and assist you in quickly and effectively achieving your goals with well-organized software solutions.


    How does Yalantis approach the data modeling process?

    We begin with getting to know your business, its internal processes, and external connections. Then we elicit requirements for your software project, which can include functionality you want your software to have or integrations that are necessary, and compose a comprehensive list of those requirements to hand over to our data modeling experts. The more detailed the requirements you share with us, the more accurate the data model we can build.

    How does data modeling relate to data governance?

    Data modeling is directly related to data management and data governance. If you have data governance procedures in place such as regular data quality checks, data modeling will go smoothly, without any risks for your business.

    What are common mistakes in data modeling?

    The most common challenges include miscommunication between key stakeholders, leading to costly workarounds and frequent changes in the data model design. Building a communication plan including all critical stakeholders would be an optimal solution to this challenge. Another common challenge is overcomplicating a data model with too many dependencies, relationships, and entities. You should analyze the feasibility of your data model during its development to avoid making costly changes at the software architecture level. The last mistake is developing a data model based on assumptions in unclear situations. Each decision in data modeling should be justified and backed up by a clear business need.   

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    The number of Internet of Things (IoT) devices worldwide will almost double from 15.1 billion in 2020 to 29 billion in 2030 according to research firm Transforma Insights. These IoT devices include a wide range of items, from medical sensors, smart vehicles, smartphones, fitness trackers, and alarms to everyday appliances like coffee machines and refrigerators.

    For IoT manufacturers and solution providers, ensuring that every device functions correctly and can handle increasing loads is a significant challenge. This is where IoT testing steps in. It ensures that IoT devices and systems meet their requirements and deliver expected performance.

    In this guide, we explore IoT testing and quality assurance (QA). Specifically, we cover: 

    • the importance of comprehensive testing for complex IoT systems
    • expert insights on creating an end-to-end IoT testing environment
    • real-world examples of how companies have succeeded with IoT quality assurance

    The vital role of IoT testing

    The success of IoT solutions largely relies on thorough testing and quality assurance. IoT testing ensures that devices work together smoothly in real-world situations, even as systems expand. It’s not just about connectivity — testing must guarantee flawless, efficient, and secure functionality despite unexpected challenges. 

    In fact, rigorous testing is necessary to:

    • confirm that functionality, reliability, and performance meet expectations, even as systems scale
    • identify security flaws and vulnerabilities that could lead to safety or privacy risks
    • build trust in the system among stakeholders and users

    IoT systems combine hardware, software, networks, and complex data flows that interact with the physical world. Given their complexity, each component requires thorough testing. Let’s take a closer look. 

    IoT systems: core components and complexity

    A typical IoT system consists of four main components, each of which should be tested. 

    • End devices (sensors and other devices that are the things in IoT)
    • Specialized IoT gateways, routers, or other devices serving as such (for example, smartphones)
    • Data processing centers (may include centralized data storage and analytics systems)
    • Additional software applications built on top of gathered data (for example, consumer mobile apps)

    Together, these components form a multi-layered architecture that constitutes every IoT solution: 

    1. The things (devices) layer includes physical devices embedded with sensors, actuators, and other necessary hardware.
    2. The network (connectivity) layer is responsible for secure data transfer between devices and central systems, handling communication protocols, bandwidth, and data security.
    3. The middleware (platform) layer is the core of the IoT system, providing a centralized hub for data collection, storage, processing, and analytics. Notable middleware platforms include Microsoft Azure IoT, AWS IoT Core, Google Cloud IoT, and IBM Watson IoT.
    4. The application layer is where users interact with the IoT system. It converts device data into useful insights through user interfaces.

    Complexities introduced by multi-layered IoT architecture

    Such a multi-layered architecture brings several complexities:

    • Diverse devices that require seamless interaction. An IoT system can comprise devices from different manufacturers, each with unique specifications, firmware, and behavior. For example, a smart home system may incorporate lightbulbs from Philips, door locks from Yale, and sensors from Bosch. 
    • Coexistence of various communication protocols. For instance, a wearable device may use Bluetooth to connect to a smartphone but need a Zigbee home gateway to link to the cloud. Since each protocol has a distinct transmission rate, ensuring seamless device compatibility with all these protocols is essential.
    • Real-world integration challenges. IoT systems must seamlessly integrate data from different platforms and devices. For instance, an industrial monitoring system must consolidate data from machines and sensors into a unified dashboard while ensuring a seamless user experience.
    • Real-time data processing. Many IoT applications require real-time data processing and large-scale analytics. For instance, a fleet management system must analyze real-time data from thousands of vehicles to optimize performance.
    • Adaptation to new standards. As new wireless protocols like Thread or IEEE 802.11ah emerge, IoT ecosystems need to adapt and evolve.

    These complexities emphasize the importance of IoT testing, and we’ll now explore various forms of IoT testing in detail.

    Crucial types of IoT testing

    The main types of testing for IoT systems include:

    • Functional testing ensures that the IoT solution works as intended by testing device interactions and user interface (UI) performance.
    • Performance testing checks responsiveness, stability, and speed under typical and peak loads.
    • Interoperability and compatibility testing ensures smooth communication between different devices, platforms, and protocols.
    • Security testing identifies vulnerabilities, weaknesses, and threats through data encryption and privacy testing, physical security testing, and other methods.
    • Usability testing evaluates real user interactions to enhance the user experience (UX).
    • Compliance testing validates adherence to industry standards and government regulations.
    • Network testing checks communications protocols like MQTT, Zigbee, and LoRaWAN.
    • Resilience testing verifies system reliability under adverse conditions like hardware failures.

    Each type of testing has its place in the product life cycle, but we’ll zero in on performance testing. This is vital for IoT systems to ensure they effectively handle real-world data, users, and connectivity demands, staying responsive as demand grows.

    Having covered the significance of IoT testing and its various types, it’s time to explore how to actually perform IoT testing.

    What does it take to test IoT solutions?

    In some sense, IoT testing is no different than testing any web or desktop software application. To spot and reproduce an issue, you need to simulate the scenario in which it occurs on devices found within the IoT ecosystem. Given the variety of IoT devices that may be part of an ecosystem, IoT testing is more difficult than simply creating a script and running it as you would test a mobile app.

    To provide IoT product testing services, QA engineers usually spend hours planning and setting up the infrastructure. Then, it takes time to hone the testing process, report on the progress, and analyze the results. Of special concern is the process of building testing standards for end-to-end testing, as QA engineers need to develop auxiliary software solutions for load testing to emulate a large number of specific devices while preserving their essential characteristics.

    How can you perform IoT testing?

    To make sure an IoT ecosystem works properly, we need to test all of its functional elements and the communication among them. This means that IoT testing covers:

    • Functional end device firmware testing
    • Communication scenarios between end and edge devices
    • Functional testing of data processing centers, including testing of data gathering, aggregation, and analytics capabilities
    • End-to-end software solution testing, including full-cycle user experience testing

    Approaching IoT testing solutions, service providers should decide on the IoT tests to apply and prepare to combine different quality assurance types and scenarios.

    IoT testing involves a full range of quality assurance services. The way engineers test the system depends on its current level of maturity, the assets available to perform testing, and the requirements formed by the product team.


    Manual QA for functional testing

    When it comes to testing IoT solutions, manual testing plays a crucial role in ensuring that all functional requirements are met. Manual QA engineers perform various tasks in IoT projects, such as:

    • setting up functionality to test on real devices as well as emulators or simulators
    • running tests on both real devices and emulators or simulators
    • balancing tests on real devices with tests on emulators or simulators

    Manual functional testing remains essential for IoT projects and is applicable in early development stages and throughout product refinement. However, due to the increasing complexity of IoT ecosystems, automation becomes a necessity for most solutions.


    Automating tests for complex IoT systems

    Here are some best practices for implementing automated testing in complex IoT systems:

    • Select the right tests to automate. Not all tests benefit from automation. Focus first on repetitive tasks that are time-consuming, frequent, and tedious to perform manually.
    • Invest in tailored tools. Choose testing platforms designed specifically for IoT protocols, devices, and architectures. Look for capabilities like network emulation and virtual device simulation.
    • Modularize for maintainability. Break tests into modular, reusable scripts. This simplifies updating as requirements evolve.
    • Simulate real-world conditions. Mimic real-life scenarios like varying network states, unexpected device failures, and different data loads. This builds resilience.
    • Integrate into CI/CD pipelines. Include automated testing in continuous integration and deployment workflows. This enables rapid validation of code changes.
    • Ensure scalability. As the IoT system expands, tests must scale smoothly, such as through cloud-based parallel testing.
    • Regularly maintain tests. Review and update tests frequently as the system changes to keep them relevant.
    • Provide detailed failure alerts. Automated tests should instantly alert developers of any failures with logs and descriptions to enable quick diagnosis.
    • Go for virtual test environments. Virtual IoT simulations allow for efficient testing without physical devices and infrastructure.
    • Incorporate real-world user feedback. Gather user feedback through monitoring and surveys. Prioritize issues based on importance, and focus on demonstrating responsiveness.

    Automation is especially valuable in the realm of performance testing. Let’s explore why.


    QA automation for performance testing

    Automation becomes a requirement as IoT systems scale. This scaling involves more devices generating vast amounts of data, often resulting in system degradation, various issues, and bottlenecks. 

    To identify and replicate issues related to system performance in scenarios with substantial data generation and sharing, we can use the following types of testing:

    • Volume testing. To conduct volume testing, we load the database with large amounts of data and watch how the system operates with it, i.e. aggregates, filters, and searches the data. This type of testing allows you to check the system for crashes and helps to spot any data loss. It’s responsible for checking and preserving data integrity.
    • Load testing checks if the system can handle the given load. In terms of an IoT system, various scenarios can be covered depending on the test target. The load may be measured by the number of devices working simultaneously with the centralized data processing logic or by the number of end devices or packets a single gateway handles. The metrics for measuring load include response time and throughput rate.
    • Stress testing measures how an IoT system performs when the expected load is exceeded. In performing stress tests, we aim to understand the breaking point of applications, firmware, or hardware resources and define the error rate. Stress testing also helps to detect code inefficiencies such as memory leaks or architectural limitations. With stress testing, QA engineers will understand what it takes for a system to recover from a crash.
    • Spike testing verifies how an IoT system performs when the load is suddenly increased.
    • Endurance testing checks if the system is able to remain stable and can handle the estimated workload for a long duration. Such tests are aimed at detecting how long the system and its components can operate in intense usage scenarios or without maintenance.
    • Scalability testing measures system performance as the number of users and connected devices grows. It helps you understand the limits as traffic, data, and simultaneous operations increase and helps you predict if the system can handle certain loads. If the system breaks, there may be a need to rework its architecture or infrastructure.
    • Capacity testing determines how many users and connected devices an IoT application can handle before either performance or stability becomes unacceptable. Here, our aim is to detect the throughput rate and measure the response time of the system when the number of users and connected devices grows.

    Running these types of performance tests becomes crucial at some point in every IoT project’s life. At the same time, checking the performance of a complex IoT system is itself a challenge, as it involves deploying complex infrastructure and programming a number of simulators and virtual devices to mimic an IoT network.


    Why we focus on performance testing

    To understand the importance of performance testing for IoT projects, let’s compare what the QA process looks like without performance testing and with a complex approach to testing.

    At Yalantis, we provide IoT testing services for projects of different scales and complexities. Among our clients are well-known automotive brands and consumer electronics companies, manufacturers of IoT devices, and vehicle sharing startups. Their experience has proved that performance testing is an essential component of the success of every IoT system.

    Read also: How we developed an IoT application for smart home management

    Embedding performance testing into the IoT development process requires a systematic approach. To make regular testing a part of your software development lifecycle (SDLC), we advocate implementing an IoT testing framework.

    Internet of Things testing framework

    An Internet of Things testing framework is a structure that consists of IoT testing tools, scripts, scenarios, rules, and templates needed for ensuring the quality of an IoT system.

    An IoT testing framework contains guidelines that describe the process of testing performance with the help of dedicated tools. In a nutshell, implementing a performance testing framework helps mature IoT projects approach QA automation in a complex and systematic way.

    Although a framework establishes a system for regular performance measurement and contributes to continuous and timely project delivery, there are cases when it’s optional and cases in which such an approach to quality assurance is a must. The latter include:

    • Projects with average loads
    • Projects with high peak loads (during specific hours, time-sensitive events, or other periods)
    • Rapidly growing projects
    • Projects with strict requirements for fault tolerance; critical systems
    • Projects sensitive to response time (solutions where decision-making relies on real-time data)

    Read also: Ensuring predictive maintenance for large IoT manufacturers

    How to implement an Internet of Things testing framework

    Quality assurance is an essential part of the SDLC. An IoT testing framework is usually implemented based on the following needs of an IoT service provider:

    • Define the current load the system can handle
    • Set up and measure the expected level of system performance
    • Identify weak points and bottlenecks in the system
    • Get human-readable reports
    • Automate performance testing and conduct it regularly

    A typical flow for the IoT testing process includes four consecutive steps.

    1. Collect business needs
    2. Create testing scenarios
    3. Run performance tests
    4. Based on the needs specified, address issues and decide on possible improvements

    A typical team for performance testing an IoT project should include the following specialists:

    • A business analyst who’s in charge of understanding the needs of the IoT business and assisting with defining the scope of usage scenarios as well as the context in which they are applicable, key metrics, and customer priorities
    • A solution architect to set up and deploy IoT testing infrastructure that covers necessary scenarios
    • A DevOps engineer to streamline the process of IoT development by dealing with complex system components under the hood (for example, implementing or improving container mechanisms and orchestration capabilities for more effective CI/CD)
    • A performance analyst (QA automation engineer) who’s responsible for engineering emulators for performance testing, running the tests, and measuring target performance indicators

    Depending on the project’s complexity and maturity, the team may be extended by involving a backend engineer, a project manager, and more performance analysts.

    What tasks can an IoT testing framework solve?

    “What our clients usually lack in their testing strategy is regular assurance and reporting. A performance testing framework can bridge these gaps and is easily aligned with the company’s business processes.”

    Alexandra Zhyltsova, Business Analyst at Yalantis

    Technically, a framework is a set of performance testing tools connected within a system to help IoT testing companies achieve the expected results, i.e. ensure stable performance of an IoT system in different scenarios.

    With an IoT testing framework, QA engineers and IoT developers receive a common tool with the following capabilities:

    1. Setting up load profiles
    2. Ensuring test scalability as the system grows
    3. Providing real-time visual representations of test results

    Let’s overview the structure of a typical Internet of Things testing framework.

    IoT testing framework infrastructure

    When deciding on the IoT testing infrastructure, a QA team starts by analyzing the given IoT system. Taking into account the specific requirements within the scope of performance testing, they decide on the tools to use and establish communication between them to receive the desired outcome, i.e. a detailed report of performance metrics. Although framework infrastructure differs from case to case, the aim is always to collect, analyze, and present data.

    A typical process of IoT testing for performance measurement within the implemented framework includes the following steps:

    • Data is collected from resources within the IoT system (devices, sensors).
    • A third-party service (Telegraf) is used to collect server-side metrics like CPU temperature and load.
    • Collected data is sent to a client-side app capable of analyzing and reporting performance-related metrics (for example, Grafana).
    • An Internet of Things testing tool for performance measurement generates the load and collects the necessary metrics (JMeter).
    • Analyzed data from both Telegraf and JMeter is retrieved and stored in a dedicated time-series database (InfluxDB).
    • Data is presented using a data visualization tool (Grafana and built-in JMeter reports)

    When is the right time to implement an IoT testing framework?

    “Of course, when the product is at the MVP stage, deploying a full-scale performance testing infrastructure is not something IoT providers should invest in. When the product is mature enough, a need to standardize the approach to testing appears.”

    Alexandra Zhyltsova, Business Analyst at Yalantis

    “The optimal time to get started with performance testing is two to three months before the software goes to full-scale production (alpha). However, you should make it a part of your IoT development strategy and put it on your agenda when you’re approaching the development phase. At the same time, there is no good or bad time to implement a performance testing framework. You achieve different aims with it at different stages.”

    Artur Shevchenko, Head of QA Department at Yalantis

    Below, we highlight some real cases where implementing a QA testing framework helped our clients prevent and solve various performance issues at different stages of the product life cycle.

    You may also be interested in our expert article about IoT analytics.

    Testing IoT: use cases

    For this section, we’ve selected stories of three different clients that benefited from ramping up their strategy by using IoT testing services. Let’s overview how implementing an IoT testing framework worked for them.

    RAK Wireless

    RAK Wireless is an enterprise company that provides a SaaS platform for remote fleet management. The product deals with the setup and management of large IoT networks, for which performance is crucial.

    Initial context. We’ve been collaborating with RAK Wireless for two years. During this time, we’ve gradually moved from situational testing to implementing a complex performance testing approach.

    When the product was in private beta, the number of system users was limited, and there was no need to get started with performance testing. In the course of development, moving to public beta and then to an alpha release, we focused on estimating the system load and understood that we were expecting an increase in the number of users and devices connected to the IoT network.

    The solution’s main target customers are enterprises and managed service providers handling tens and hundreds of different IoT networks simultaneously. For such customers, performance is one of the key product requirements.

    With more customers starting to use the product, the number of IoT devices was growing as well. To prevent any issues that could appear, the client considered changing the approach to IoT testing from ad hoc to a more complex one.

    Challenges. The product handles the full IoT network setup process, from connecting end devices to onboarding gateways and monitoring full network operability.

    1. The first challenge was to carefully indicate the testing targets and plan test scenario coverage. We chose a combination of single-operation scenarios and complex usage flow scenarios to define the metrics to collect at each step.
    2. Once test coverage was planned, another challenge was to properly emulate a number of proprietary IoT devices of different types to make sure we were using optimal infrastructure resources for a given scenario. We ended up creating several different types of emulators. For some scenarios, a data-sending emulation was enough; others required emulating full device firmware to replicate entire virtual devices.

    What we achieved with the performance testing framework. We aimed to stay ahead of the curve and detect performance issues before any product updates were released. Implementing the testing framework allowed the product team to take preventive actions and plan cloud infrastructure costs. Transparent reporting and optimal measurement points allowed us to quickly report detected issues to the proper engineering team in charge of testing firmware, cloud infrastructure, or web platform management.

    Automating performance testing allowed us to achieve coherence and ensure regular compliance system checks. A long-term performance optimization strategy helped us achieve and preserve the desired response time, balance system load by verifying connectivity with device data endpoints, and ensure system reliability under normal and peak loads.


    Initial context. Our client is a member of Toyota Tsusho, a corporation focused on digital solutions development and a member of the Toyota Group.

    One of the products we helped our client develop was an end-to-end B2B solution for fleet management. Vehicles were equipped with smart sensors that connected to a system to send telemetrics data using the TCP protocol.

    Some of the features provided by the IoT solution included:

    • Building and tracking routes for drivers
    • Tracking vehicle exploitation details
    • Providing real-time assistance to drivers along the route
    • Providing ignition control

    Challenges. Taking into account the scale of the business, the client needed to maintain a system that comprised about 1,000 end devices working flawlessly in real time.

    Among the tasks we aimed to solve with the testing framework were:

    • Ensuring the delay in system response to data received from devices didn’t exceed two seconds
    • Preventing any breakdowns related to the performance of sensors tracking geolocation, vehicle mileage, and fuel consumption
    • Establishing a reporting system that would allow for processing and storing up to 2.5 billion records over three months

    What we achieved with the performance testing framework. We created a simulator to simultaneously run performance tests on the maximum number of end devices.

    The framework included capacity testing and allowed results to be represented by providing useful reports that display a specified number of records.

    Running volume tests helped us spot product infrastructure bottlenecks and suggest infrastructure improvements.

    Miko 3

    Initial context. Our client is a startup that produces hardware and software for kids, engaging them to learn and play with Miko, a custom AI-powered robot running on the Android operating system. We were dealing with the third version of the product, which quickly became extremely successful. The client was doing some manual and automated testing to detect and fix performance issues. But the lack of a systematic approach to quality assurance resulted in system breakdowns during periods of peak load.

    Challenges. Issues with performance appeared after the company sold a number of robots during the winter holidays. When too many devices were connected to the network, customers started to report various issues that prevented their kids from playing with the robots. It became obvious that the system was not able to handle the load.

    The client understood that they needed to adjust and expand their performance testing strategy, as the product was scaling and the load was only expected to grow further.

    What we achieved with a performance testing framework. We aimed to establish a steady process of load testing for both the main use cases (such as voice recognition for Miko 3) and typical maintenance flows (such as over-the-air firmware updates).

    Implementing the performance testing framework allowed us to build a system capable of collecting and analyzing detailed data about the system’s performance. Having this information, we managed to define the system’s limitations and detect bottlenecks in the architecture design as well as plan architectural optimizations.

    Such an approach allowed us to systematically predict system load by measuring response times and throughput rates and analyzing error messages. A stable reporting process helped to distribute responsibility among the IoT testing team and optimize development costs.

    Wrapping up

    In a mature multi-component IoT ecosystem, performance testing is as crucial and technically demanding as end-to-end IoT testing.

    The efforts invested in setting up a comprehensive testing framework with the required IoT application testing tools will pay off in detecting performance issues and improving the user experience before you get negative feedback from customers.

    Such an approach to quality assurance brings transparency to system resource use and bottlenecks, allows you to estimate the system’s effective load capacity and scalability potential, helps you optimize infrastructure and costs, and supports decisions about improving the solution architecture.

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    What is the main challenge of IoT device testing?

    The main challenge of testing IoT devices and gateways is their diversity. For the majority of reasons, QA engineers would need to emulate them. This takes time and requires the involvement of a highly skilled IoT development team.

    What is the most cumbersome part of IoT testing and what corresponding measures to take?

    The most cumbersome part of IoT testing is related to testing edge devices and gateways. In fact, for testing edge devices and IoT gateways, QA engineers perform the same test types they would do for traditional software testing. To test the network level (gateways) you would usually need functional, security, performance, and connectivity tests. To perform QA of edge devices, functional, security, and usability tests are required. Additionally, there is a need to run compatibility tests.

    What is an Internet of Things testing framework?

    An IoT testing framework is a structure that comprises IoT testing tools, scripts, scenarios, rules, and templates required for providing the quality of an IoT system. 

    Machine learning (ML) can enable a business to harness the power of data to drive innovation. A successful ML solution can achieve revenue growth, offer a competitive advantage, improve automation, bring efficiency gains, and provide actionable insights.

    Statista predicts the ML market will experience a compound annual growth rate (CAGR) of 18.73 percent between 2023 and 2030, leading to a market volume of $528.10 billion by the end of that period. This strong growth creates the feeling that literally every business needs to implement ML — especially when there are so many widely discussed use cases. So, is ML the magic pill for your business needs? To determine if ML can help you achieve your business goal or solve a business problem (and how), you need to understand:

    • how this technology works
    • its place in the hierarchy of artificial intelligence (AI) technologies
    • its main capabilities and limitations based on examples of machine learning use cases

    This knowledge is critical, as there’s a lot of confusion about AI and ML that leads to:

    Overgeneralization. AI and ML are often used as broad terms encompassing various technologies, leading to misunderstandings of their specific capabilities and limitations.

    Unrealistic expectations. Due to media portrayals and hype, it’s a common misconception that AI and ML can solve any problem, leading to disappointment when the technology’s actual capabilities are understood.

    Skepticism. AI and ML are complex topics. A lack of understanding can lead to skepticism about them and unjustified refusal to implement them even when they are likely to be highly beneficial.

    That said, you don’t need broad knowledge of ML to check if it can be beneficial for your company or a specific business case. To figure that out, just read this article, which describes common machine learning use cases and offers an overall roadmap for setting up and implementing a successful ML-driven project.

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    The role of ML in the AI landscape

    Let’s figure out what AI, ML, and deep learning (DL) are at a basic level, which is sufficient for our purposes.

    Artificial intelligence refers to systems capable of performing tasks that typically require human intelligence. ChatGPT, a language model that has recently become the stuff of legend, is a great example of an AI use case. ChatGPT can process and understand text, engage in dialogue, and provide intelligent human-like responses.

    Machine learning is a subset of AI focused on designing algorithms and models that automatically learn patterns, make predictions, or take actions based on data. In an ML-driven project, a set of algorithms and models are fed with structured data to carry out a task without being programmed how to do so. When it’s effective, a trained ML algorithm uses data to answer a question, and this answer can be accurate and impossible even for human experts to provide.

    Deep learning is a specialized subset of ML that concentrates on training artificial neural networks with multiple layers (deep neural networks). ChatGPT uses advanced DL techniques to understand and generate human-like text based on complex patterns and relationships it has learned from training on vast amounts of textual data.

    Is ML suitable for your business case?

    Here is a list of the main preconditions for a successful ML-driven project to answer the question above:

    1. Set specific goals and identify the key problem  

    Having clear project goals and a well-defined problem statement provides focus and direction, enabling effective planning, data collection, algorithm selection, and evaluation of results. It helps align stakeholders’ expectations, ensures efficient resource allocation, and facilitates communication and collaboration throughout the project.

    For example, the goal of an ML-driven project might be the development of a predictive model that accurately detects fraudulent transactions in real time for an e-commerce platform, reducing financial losses and improving customer trust. In this case, the problem statement might be leveraging historical transaction data to train an ML model capable of identifying fraudulent transactions with a high level of accuracy, minimizing false positives and false negatives.

    2. Make sure you have enough quality data

    Having a significant amount of quality data is critical for achieving accurate and reliable results in AI-driven projects. This need is especially acute for ML-driven projects where sufficient high-quality data allows the model to capture a wide range of patterns, relationships, and variations present in the data. A large amount of quality data provides the foundation for building accurate and reliable ML models. On the contrary, insufficient or low-quality data can result in incomplete or biased learning, leading to suboptimal performance.

    You can understand if you have enough reliable data for your ML-driven project by evaluating the volume, quality, relevance, distribution, and accessibility of available data in relation to the project’s requirements and the performance expectations of ML algorithms.

    3. Rule-based or ML: What’s your cup of tea?

    There are cases when the rule-based approach is preferred over the ML approach for problem-solving and decision-making. Rule-based systems operate according to a set of predefined rules programmed by humans. These rules define the logic and decision-making process and are typically in the form of if–then statements, where specific conditions (if) lead to predetermined actions or outcomes (then). Rule-based systems are well-suited for problems with clearly defined and predictable patterns, where human expertise and domain knowledge can be explicitly encoded into rules. For example, quality control, medical diagnosis, and workflow management systems might be rule-based.

    See that your problem can be solved with a rule-based approach? Then go for it. It will allow you to build a transparent and explainable system. ML models are typically more complex. Moreover, taking the ML approach in such a case would be counterproductive, since it requires extensive data, training, and computational resources to learn patterns and make predictions. But you should choose the ML approach if you need to deal with complex patterns, large datasets, unstructured or ambiguous data, and adaptive systems.

    To help you conclusively determine if the ML approach is effective for your business case, let’s look at the most common machine learning business use cases based on business type and size. Most likely, one of them will intersect with your project idea.

    Common ML use cases for a successful ML-driven project

    Machine learning is well-suited for the use cases below because such projects benefit from identifying complex data patterns, the use of large datasets, and the ability to learn from examples. 

    Fraud detection and risk management. This is one of the most common uses for machine learning. In finance, insurance, and e-commerce, ML can help identify fraudulent activities, detect anomalies, and assess risks. 

    Predictive maintenance. ML can be used for predictive maintenance in the manufacturing, transportation, and energy sectors. By analyzing sensor data and historical maintenance records, businesses can predict equipment failures, schedule proactive maintenance, and minimize costly downtime.

    Natural language processing (NLP) helps to analyze and get insights from textual data. NLP tasks include text classification, named entity recognition, and language translation. 

    Computer vision. This expertise allows businesses to use ML for visual recognition, image analysis, and object detection tasks. Computer vision is applicable in healthcare, retail, manufacturing, and security, enabling businesses to automate processes, enhance quality control, and improve the user experience.

    Speech recognition. ML models can be trained to turn spoken language into written text. This can improve voice assistants, transcription services, and voice-controlled systems. Speech recognition is applicable in customer service, healthcare, automotive, transportation, and other industries.

    Traffic pattern prediction involves utilizing ML algorithms to predict traffic conditions and patterns in transportation networks. By analyzing historical traffic data, real-time sensor data, weather conditions, and other relevant factors, ML models can predict traffic flow, congestion, travel time, and potential bottlenecks.

    Algorithmic trading. ML algorithms can help analyze vast volumes of financial data and make automated trading decisions by identifying patterns, trends, and correlations in market data to generate trading signals, execute trades, and manage portfolios.

    Sentiment analysis. As a subset of NLP, sentiment analysis specializes in interpreting the sentiment or emotion behind text data, such as social media posts and customer reviews. ML models are trained to analyze language, context, and linguistic cues to define if sentiment is positive, negative, or neutral. Sentiment analysis has applications in market research, brand monitoring, customer feedback analysis, and reputation management.

    Email monitoring involves leveraging ML techniques to analyze and process email communications for various purposes, such as filtering spam, detecting malicious content, categorizing messages, and extracting valuable information.

    Customer journey optimization. The use of ML algorithms can help to analyze and optimize the end-to-end customer journey across various touchpoints and interactions with a business or brand to improve the customer experience, customer satisfaction, and business outcomes.

    Complex medical diagnosis. ML algorithms and techniques can be used to assist in diagnosing patients with conditions such as skin cancer. ML models can be trained on large datasets containing medical records, patient information, symptoms, and diagnostic outcomes to learn patterns and identify correlations.

    Recommendation engines. ML algorithms can help online businesses offer users personalized recommendations. Recommendation engines analyze user preferences, behaviors, and historical data to suggest relevant items or content that users are likely to be interested in. Recommendation engines are widely used by e-commerce platforms, streaming platforms, news aggregators, and social media platforms.

    If the preconditions and use cases for machine learning above have convinced you that the ML approach is the way to go, it’s time to learn the specifics of implementing ML depending on the size of your business.

    Peculiarities of ML implementation for businesses of various sizes

    Implementing machine learning can look different for businesses of different sizes due to variations in available resources, budgets, data volume and quality, technical expertise, and organizational complexity. The bigger a business is, the more challenging it becomes for it to ensure smooth data integration due to the increasing number of internal systems and data as well as the need for its synchronization.

    Small businesses typically have limited resources and need to prioritize specific machine learning cases that align with their budget, data availability, and capabilities. They tend to build targeted solutions (demand forecasting, fraud detection, customer segmentation, sentiment analysis, and others) to gain a competitive advantage.

    Midsized businesses try to grow their technical capacity for improved scalability and innovation. To achieve this goal, they can implement predictive analytics, NLP, image or video recognition, process optimization, and anomaly detection solutions.

    Large businesses or enterprises may have dedicated AI departments along with sufficient resources and enough relevant data to benefit from the enterprise-wide impact of ML-related initiatives. Such a large-scale impact might boost decision-making across departments and functions and improve operational efficiency and risk management. Moreover, enterprise-wide ML implementations can even drive the development of new products, services, and business models.

    No matter how large your business is and what ML use case you want to implement, you need an effective plan for implementing it. Further, we talk about how to plan and execute a successful ML project.

    ML project lifecycle: steps on the road to effective implementation

    The ML project lifecycle refers to the sequence of stages and activities involved in the development and deployment of an ML project.

    1. Define the problem and set project goals. Analyze your business and determine where ML can be effectively used. The main purpose here is to define the objective and scope of the ML project. Consider your limitations, including your budget, timeline, available expertise, and available data. Make a detailed overview of the case you want to solve with ML. Articulate the problem statement and establish goals, metrics, and success criteria.
    2. Collect data. Data collection involves gathering the relevant data required for the project. This can include acquiring existing datasets, collecting new data, or a combination of both. Data should be representative, diverse, and of sufficient quality to ensure accurate model training and evaluation.
    3. Analyze and clean the data. Analyze the collected data to gain insights and identify any issues or inconsistencies. Data cleaning involves handling missing values, removing outliers, addressing data inconsistencies, and transforming the data into a suitable format for analysis.
    4. Perform a feasibility study. Conduct a feasibility study to assess the viability of applying ML techniques to solve the defined problem. This involves evaluating factors such as data availability, computational resources, expertise, time constraints, and potential ethical or legal considerations.
    5. Develop and train the model. Design and develop an ML model based on the problem statement and data. Split the data into training and validation sets, and train ML algorithms on the training data to learn patterns and relationships. Iteratively refine the model to improve its performance and ability to generalize.
    6. Fine-tune the model. Fine-tuning involves optimizing the model’s hyperparameters — such as learning rate, regularization, and network architecture — to achieve better performance. This step aims to strike a balance between underfitting and overfitting, ensuring the model can effectively generalize unseen data.
    7. Integrate the model. Once the model is trained and fine-tuned, integrate it into the desired application or system. This involves setting up the necessary infrastructure, APIs, or interfaces to incorporate the model’s predictions or insights into the target environment.
    8. Refine the model. Continuously monitor and evaluate the model’s performance in a real-world setting. Feedback from users, performance metrics, and ongoing data collection can help you identify areas for improvement. Refine the model by incorporating new data, retraining the model, or updating algorithms to enhance its accuracy and relevance.
    9. Monitor and maintain the model. After deployment, regularly monitor the model’s performance. This involves tracking key performance metrics, detecting any drift or degradation, and conducting periodic maintenance and updates to ensure the model’s effectiveness, reliability, and alignment with changing requirements.

    During the first step of the ML project lifecycle, you’ll need to choose an optimal technology stack. That’s when the project strategy decision tree will serve you well.

    How a project strategy decision tree helps to create a workable ML solution

    A project strategy decision tree is a visual representation or diagram that guides the decision-making process when developing a project strategy. It presents a hierarchical structure of decisions and their potential outcomes, allowing project managers and teams to systematically evaluate and choose the most appropriate strategies based on specific criteria.

    Using a project strategy decision tree, you can systematically evaluate different technology stacks, consider relevant criteria, and make informed decisions that align with the specific needs and goals of your ML-driven project.

    There are two common approaches to the technical implementation of an ML-powered project:

    1. If you don’t have in-house ML expertise and you need to handle a common ML use case, use a suitable SaaS solution (such as IBM Watson Studio, Databricks, or DataRobot).
    2. If you need to quickly set up the required infrastructure, access pre-built tools and frameworks (such as TensorFlow, Keras, PyTorch, and Caffe), obtain computing power and resources, and validate the feasibility of your AI and ML initiatives, use AI and ML infrastructure or managed services (such as AI managed services by Amazon, Microsoft, or Google).

    Having viewed the overall approach to implementing an ML-driven project, let’s see how to build an appropriate architecture for an ML solution based on a fictional example.

    Designing an optimal architecture for an ML project

    Imagine you have a blog with over 500 articles and around 1,000 unique visits per day. The problem is that only 0.5 percent of visitors fill out the contact form. During initial communication with potential clients, most ask for articles showcasing your expertise even though they are already available on the website. While searching for services, potential clients often miss such articles and may think that a company doesn’t have the required experience. To solve this problem, you can benefit from ChatGPT’s capabilities. Follow the steps described below.

    1. Create an architectural vision

    To develop the solution architecture, you need to create an architectural vision, which is the high-level view and strategic direction of the architecture, outlining its desired future state, goals, and principles. An architectural vision can be created based on the most important architecture drivers: business goals, use cases, and constraints. For this project, they will be as follows:

    Business goals:

    • Decrease the time for lead conversion by 50 percent.
    • Increase the sales team’s efficiency by providing a convenient article knowledge base.

    Use cases:

    • Prospects can use a chatbot on the website to find relevant articles.
    • The sales team can use a chatbot in Slack to find relevant articles by keywords.


    • Implementing the project should be cost-effective.
    • The system should be developed by one engineer within two months.

    2. Consider architectural concerns

    You should take into account all factors that might affect the future solution’s quality and feasibility. Here are the architectural concerns for the project we’ve described:

    1. To ensure that the chat provides accurate information, ChatGPT by OpenAI should be trained with your derived data. Without it, ChatGPT will provide answers that are too generic and may be irrelevant to your company. 
    2. Training data should be verified and fine-tuned by the sales team to ensure that the chatbot broadcasts your corporate tone of voice. 
    3. ChatGPT is known to be bad at finding links to existing pages (consequently, there is a need to validate and regenerate links after ChatGPT).

    Suppose you decided to create a proof of concept (PoC) to verify the feasibility of adding training data to ChatGPT and check the accuracy of links it provides to your corporate website. The developed PoC helped you make sure that links generated by ChatGPT lead to 404 pages in most cases, requiring you to validate and regenerate them.

    3. Develop the business architecture

    A business architecture diagram shows all the systems we have within the project (we will create some of them from scratch and the rest will be ready-made solutions). The diagram should also include all actors working with these systems. The following is an example of what the business architecture for the described project might look like:

    4. Resolve the dilemma of adopt or build

    Next, you need to decide which existing solutions (paid or open-source) to adopt and which to build yourself. We advise creating custom solutions only if they have the potential of bringing you a competitive advantage or enabling you to get more profit or clients. Otherwise, use ready-made solutions since the less code, the shorter the time to market and the fewer development bottlenecks you’ll face in the future.  

    For the project described, we would advise you to create the following solutions:

    • Website chat UI to ensure strong company branding and provide a better user experience than competitors
    • A training data set to ensure the chatbot transmits your corporate tone of voice while communicating with prospects 
    • An API to connect all adopted solutions, since you are unlikely to find a proper ready-made solution for this purpose

    5. Choose the most appropriate solutions

    Use a decision log, technology radar, and reliable metrics to make the final decision on the best technology solution, be it a framework or a programming language.

    1. A decision log is a record of the decisions made during the process of evaluating and choosing the most suitable technology solution for a specific purpose. It includes information about the technologies considered, evaluation criteria, decision rationale, stakeholders involved, and any supporting documentation or references.
    2. Technology radar is a tool used to track and assess emerging technologies, trends, and practices in the software development industry. 
    3. Other parameters to consider include the number of stars on GitHub, containers in Docker Hub, questions on Stack Overflow, and freelancers on Upwork.

    6. Identify all risks (tradeoffs) in your architectural solution

    Perform tradeoff analysis to evaluate and make optimal decisions about various architectural options by considering the tradeoffs associated with each choice. This involves assessing the advantages, disadvantages, and impacts of different architectural decisions to identify the optimal solution for a specific software system.

    The table below is the result of conducting tradeoff analysis for the project described in this post. Each row of the table is an architectural decision (AD). Each column is an architectural driver (a key requirement or consideration that influences the design and development of an architectural solution) outlined at the architectural vision stage: business goals (BG), use cases (UC), concerns (CN), and constraints (CT).  

    Marks at the intersection of rows and columns characterize the architectural decisions as follows:

    • S (sensitive) — AD is critical to ensure, as the driver won’t be met if it isn’t
    • N (non-risk) — AD is optional to ensure, as the driver will be met even if it isn’t
    • R-1 (risky) — The sales team has to verify the spreadsheet data, which might be time-consuming
    • T-1 (tradeoffs) — Refining links using Google Search API is obligatory but will prevent meeting the project deadline

    As you can see, implementing a successful ML-driven project is a complex process that might require years of expertise and accumulated knowledge. If you decide to cooperate with an AI and ML development provider, pay attention to their expertise in ML algorithms and techniques, portfolio of AI-powered projects, and professional recognition. Yalantis has been proclaimed a leading AI development company by C2Creview, a research and IT company review platform. We consult and create quality software utilizing AI, data science, and business intelligence and analytics.

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    For software developers, an end user license agreement (EULA) is a written contract that needs to be included as part of the distribution of your software.

    Its job is to protect the license owner from misuse of their product, limit liability, and set out restrictions for the use of the software.

    What is an end user license agreement? 

    An EULA is essentially a contract between users and software developers. It sets out conditions, rules, rights, and responsibilities for all parties in the same way terms and conditions or terms of service do.

    This contract gives the end user the right to use the software but makes it clear the developer still owns it—after all, if you’ve hired the best app developers to create your product, you want to protect it.

    Users are typically asked to agree to the EULA before being able to download and install a software application. This ensures they’re bound in agreement with the terms set out in it before being granted access to the software license.

    Who needs an end user license agreement? 

    To put it plainly, if your software is made available for public use, you need an EULA.

    We live in an increasingly digital world, and the tech industry is continually expanding and developing. Many businesses in all sectors provide software applications, and software such as client portal solutions are becoming more commonplace.

    Most modern companies—and also those thinking of starting a business—must now consider the need for an end user license agreement.

    Is an end user license agreement a legal requirement? 

    An EULA is not required by law, but you’re likely to face some legal hurdles if you don’t have one.

    Although it’s not a requirement, an EULA is legally binding—because it acts as a contract between the developer and the end user, contract law applies.

    For businesses seeking software development, outsourcing to an offshore software development team is a common option. Understanding the need for an end-user license agreement and its benefits is essential in these cases. It is important to choose agile project management tools to ensure smooth collaboration between the development team and the business.

    Free-to-use image sourced from Pixabay

    Five reasons to have an end user license agreement

    There are many reasons why having an EULA is advantageous for software developers. Good program management and strategic planning should include creating one that is effective in all situations.

    Here, we look in more depth at five key reasons to include an EULA with your software app.

    1. An EULA sets out the restricted use of your app

    A clear advantage of having an EULA is that you can set out how your software should and should not be used. Even if your business offers digital storefront software, you need to get a EULA for your customer so they know how to use the software properly without breaking any laws/rules. For example, you’ll probably want to state that it can’t be used for illegal activity or activity that violates copyright law.

    In this clause, you’d typically see statements saying the user can’t do the following:

    • Use the software in a way in which it’s not intended to be used

    • Copy or reproduce it in any way

    • Use it for illegal purposes

    • Distribute it to third parties

    2. Protect your intellectual property

    Software developers should ensure their work is used only in the way it was intended, so it’s important to set out restricted use in your EULA. You should also protect your intellectual property.

    Software developers may find it helpful to use a standard NDA template to ensure employees and other stakeholders can’t copy, steal or recreate the software for their own use.

    In your EULA, write a clause that protects your content and establishes it as yours. You should outline your ownership of this copyrighted property and explicitly state what’s yours and how users may or may not use it.

    Your EULA should make clear that the end-user has access to a copy of your software (or a license to use it) but no rights to the software itself.

    Below is an example of a copyright clause from Whatsapp that clearly states their ownership rights.

    Image sourced from

    3. Limit your liability

    When writing this clause in your EULA, it may be useful to refer to a liability waiver template. You want to protect your business by getting the user to agree that they can’t pursue a legal claim against you in the event of damages or loss.

    For software developers, an example would be if a user downloads and installs your software, and this results in their device malfunctioning in some way. Having a clause in your EULA that limits your liability will prevent them from taking legal action against you.

    Include a clause that ensures users accept any risk by agreeing to the EULA before downloading and installing your software.

    4. Protect your right to terminate licenses

    Your EULA should make it clear that you can terminate the agreement at any time without notice if the end user breaches your terms.

    For example, if a user takes part in an activity that’s prohibited in the “restricted use” section of your EULA, you as the software provider have the right to terminate their license immediately.

    This clause lets users know that they don’t have unlimited or unrestricted access to the license and that you can revoke it at any time given sufficient reason.

    5. An EULA can assist in dispute resolution

    Including information on dispute resolution in your EULA is also useful. Here, you want to include how disputes between you and your customers will be resolved and which laws/legal system the dispute will be governed by.

    The terms stated in your EULA must be agreed to by your end user before they can use your software. Ensure they include comprehensive information that you can rely on to defend your product if a dispute arises.

    Free-to-use image sourced from Pixabay

    Displaying your EULA 

    Ideally, your user should agree to your EULA before they purchase or download the software or app. Whether your business offers video recording software or other software solutions, you need to ensure the users have agreed to your EULA. This can be achieved by displaying it pre-download and ensuring you state that upon downloading it they’re agreeing to your terms.

    Your EULA must be easy to find and not hidden in any way. Users should also be able to access it at a later date if they want to refer back to it.

    The Importance of an End User License Agreement 

    An EULA is an important—even essential—agreement for software developers. While they will differ depending on the product, they should include the following information as standard:

    • The licensor. Contact information for the software owner.

    • A warranty disclaimer/liability limitations. I.e. the software developer is not responsible for damages or losses resulting from downloading the software.

    • Copyright information. The EULA should clearly state ownership information.

    • A start date. It should make clear that the agreement is in place from the time the software is downloaded.

    • User restrictions. These should set out prohibited or restricted use of the software.

    • Termination. State that the licensor has the right to terminate the license if certain terms are breached.

    With an effective EULA, you can confidently market your software to end users, knowing your ownership rights are protected, its intended use is clear, and your liability is limited.

    Need IT expert advice to help you business prosper?

    Yalantis is here to help

    Contact us

    Organizational methods allow businesses to observe practices and activities from a granular level. Understanding where to prioritize the allocation of time and resources can help a business operate more efficiently and effectively.

    Strong program management is vital to ensuring that your organization excels and meets its strategic goals. We’ve talked to top Yalantis program managers and created an article where you can find tips, best practices, and approaches to program management.

    This expert material will be useful if you:

    • plan to prepare a large product in a certain time frame with a large number of people and unclear processes
    • need to develop and/or scale a product with a large number of components
    • have a project where are already more than two teams

    What is program management?

    Program management is a strategic management approach to executing and controlling multiple related projects. It aims to drive benefits to the entire program by sharing project resources, costs, and activities.

    The key value of program management is in effectively managing resources among projects within a program and achieving your company’s overall strategic goals.

    Program management involves the following focus areas:

    • Planning
    • Risk management
    • Stakeholder management
    • Performance management
    • Organizational change management
    • Communication management and governance

    The program management framework does not exclude the project management framework but rather complements it. However, program management consolidates all components and has additional tools and mechanisms that allow you to keep and manage multiple projects as one entity.

    What is a program in program management?

    A program can develop in two ways. First, a large company can create a program from the very beginning of developing a product and fill it with small connected projects. The second way, which Yalantis often encounters in practice, is that a program grows out of a project, passing through certain stages which we will describe below. Either way, the golden rule is that a program is always much bigger than a project.

    What a program is at Yalantis

    It’s important to note that the definition of a program will differ between companies and program management offices (PgMOs).

    At Yalantis, we distinguish two basic principles of a program in program management:

    • A program necessarily contains several subprojects, i.e. components united by a certain principle: a common code base, a common client, etc.
    • A program is never defined by the number of people.

    It is easiest to demonstrate what a program is at Yalantis by comparing a program to a project. First, it is always more difficult to manage resources in a program, as a program always involves more teams than a project. Second, when managing a program at Yalantis, we are more directly managing business development. That means we ask ourselves what benefits the program brings to the client and the client’s company.

    Using various stakeholder management techniques while working with stakeholder groups, we determine how to achieve the program’s benefits. At the project level, project management is only about the schedule, scope, and cost.

    When working on a program at Yalantis, our experts give our clients the opportunity to develop the product faster due to our ability to distribute the work across large teams and work on fast-tracking and crashing methods, which involves fast scaling if needed.

    All in all, Yalantis program managers understand how to make a big product with a large team in a relatively short time.

    You may also be interested in our expert material on how to deal with strict project limitations.

    Willing to see how we approach software development to drive performance, scalability, maintainability, and rich functionality?


      What is the value of a program manager? 

      Program managers possess a flexible set of skills that can be adapted to different business environments over time. They usually offer firm strategic advice to ensure every project in a program can be successfully executed. With extensive knowledge banks, the practices they decide to execute determine the blueprint for program management and its overall success.

      Let’s demonstrate the program manager’s responsibilities by considering how they differ from the responsibilities of a project manager. A project manager generally thinks about current project goals, while a program manager thinks about the program strategy, the future, the number of program components, and how to allocate resources correctly if there are new components.

      The program manager synchronizes projects that are managed by individual project managers.

      Read also: What a project manager does at each stage of the software development process

      We can summarize a program manager’s responsibilities as follows:

      • Understand the big picture without going into details
      • Focus on future goals
      • Pay much attention to the integration of program components
      • Work with the program budget
      • Be responsible for daily management through the life cycle of the program
      • Plan the overall program and monitor progress to ensure that milestones are met across various projects and programs
      • Manage program risks and issues that might arise over the course of the program life cycle and takes measures to correct them when they occur
      • Coordinate projects and their interdependencies between various projects in the program

      At Yalantis, we exceed our clients’ expectations by taking into account business needs. We determine why the client desires to create particular functionality. Thus, we better serve the business development processes on the client’s side and efficiently cover the client’s needs.

      Program management at Yalantis

      Below, we describe our approach to building successful program management.

      Our approach involves using a hybrid of classical and agile management techniques. Namely, we keep the feedback loops and quick releases of agile while achieving the budget and schedule predictability of classical techniques. Let’s take a closer look at this approach:

      Creating a program approach

      #1. Defining goals and benefits is the very first step we start a program with. Stakeholders and executives come together to produce a program strategy (program charter) covering the vision, scope, minimum objectives, budget estimation, resource management, and benefits. The brief is passed to the program manager to identify program contributors, project dependencies, risk factors, scheduling, and technical requirements.

      #2. Collecting customer requirements is our next step towards successful management of the program, and this is what we focus on most. Collecting requirements allows us to understand the needs, expectations, and constraints of the client at the very beginning. This, in turn, allows us to form the program scope.

      Read also: What role a business analyst plays at our company

      #3. Choosing the framework and the actual approach to program management is the step we take after we collect all the client’s requirements. Yalantis chooses among several commonly used frameworks including the Scaled Agile Framework (SAFe) framework, Nexus framework, and Scrum@Scale framework.

      An experienced program manager selects a framework that meets the client’s needs, and the main task of the program manager is to adjust program processes to the framework.

      When Yalantis program managers see that no classical framework will work for a particular program, they are able to create their own custom approach based on existing frameworks and adapt knowledge to the specific needs of a particular client.

      Building the structure

      Once the program scope has been defined, a program manager, together with a business analyst and a solution architect, can efficiently distribute the entire scope of work across program streams.

      At Yalantis, there are always project teams and design teams. A project team does what is needed now, while a design team does what will be needed in the future.

      The program manager leads the design team, and together they figure out how to build processes so that all components and all stakeholders work as a united mechanism.

      In addition, a design team has its own specific workflow. For example, a project team has the following workflow: to do – in progress – in review – done. A design team is focused on working with requests and on interacting with stakeholders. Below, you can see an example of a workflow for a design team.

      By using a program board with all requests throughout the program, we can allocate requests to different projects in the program. What projects receive within the program is a well-established process, with a clear impact, which is predefined by the program manager.

      Work with dependencies, intersections, and limitations of projects is carried out at the stage of creating the program board. Yalantis managers always work to minimize project dependencies.

      Decision-making in program management

      The decision-making stage is one of the main stages of program management. In order to identify the main decision-maker in the program, we use the concept of a technical lead. After the design team has developed a backlog for the release, it should be reviewed by the frontend, backend, QA, and mobile technical leads.

      Below, we can see part of the internal SDLC of the design team:

      A tech lead’s responsibilities as a decision-maker at the stage when the backlog is still on the design team’s side are the following:

      • Make sure all backlog tasks are possible to execute
      • Simplify the solution as much as possible
      • Make sure that product backlog items (PBIs) will really bring the value the client needs
      • Check what can be reused to save resources (third-party products, libraries, company’s developments)

      After validating all the tasks with technical leads, a program manager makes a high-level release estimate, after which the estimate is approved by the client.

      Creating program documentation

      Program documentation differs from project documentation because the focus of the program is on strategy, communication with stakeholders, and the RAID log ((R)Risks, (A)Assumptions, (I) Issues, and (D) Dependencies). Also, the program is more focused on resource planning and road mapping. Accordingly, the must-have documentation is the following:

      • Program strategy (program charter)
      • Program RACI matrix
      • Communication plan
      • Program RAID log
      • Resource allocation and budget
      • Program roadmap

      Yalantis tips on documentation in program management:

      1. Keep checklists flexible. Unlike a project, there is a constant release cadence in a program. Accordingly, there is a constant need for checklists and constant checks.
        We use no strict and inflexible rules. All teams have templates and knowledge bases for introducing checklists for processes. We also reserve the right for teams to adjust the writing of documentation themselves in a way that is convenient and customized for a particular project.
      2. Master the roadmap. Our roadmap allows each team to see its workload as needed while allowing stakeholders and program managers to see the whole picture and all dependencies, and to catch problems within the program.
        It’s worth noting that we have a rather flexible format for the roadmap, not the classic orthodox format. This allows us to immediately make changes and new inputs if they arise in the process of program management.

      Yalantis’ key principles for successful program management

      Yalantis program managers create custom processes in the program workflow. At the same time, there are many best practices that managers are constantly implementing, adjusting, and improving. Some examples we can mention below without using the information from projects covered by NDAs:

      1. Decentralizing the program to eliminate vertical management in the team. Formally, the vertical exists, but in practice, each project manager within the program is isolated. In reality, this means that each project manager has their own context and priorities. They interact with other projects only when they go to release some feature, and only to the level that is critically necessary.

        We delegate everything that can be trusted to the discretion of the project team. Only when there is an intersection of teams or complex milestones do we enable program management tools. Due to this, each team is able to make its delivery without dependence on the central office, which would slow down processes.

      2. Improving release management includes:
        1. Separating the release manager role. This is a technical specialist who ensures the accuracy of the development team.
        2. Using release calendars. By doing this, we see all tasks that are already scheduled by releases, with dates and progress bars.
        3. Developing on cadence, releasing on demand. Our release calendars are based on the principle that a release should occur every two weeks. Thus, the team knows that in two weeks there will be a release in any case — and it is up to the team what they put in the release and what they do not.
      3. Implementing a core team into the workflow. The scope of work for every program includes work with innovations and a proof of concept (POC). We have the option to prioritize this scope as a backlog in existing teams or to separate teams that will focus solely on the abovementioned tasks.
        At Yalantis, we use the second approach. We have core teams that mostly consist of backend engineers, DevOps, and QA specialists. This allows us to guarantee the client regular releases and regular deliveries within a large, scalable program.
      4. Focusing on dependency management. In order not to have a situation where a manager is burdened with all the tasks, we build effective communication between project managers, leaving the program manager as a facilitator and a decision-maker in conflict situations.
      5. Establishing a program management office (PgMO). Due to the program’s complexity and scope, this requires the support of a centralized responsible entity: the program management office. The PgMO generally has several members. Normally, more than one program manager is needed to handle all the demands.

      Mature PgMO in Yalantis

      At Yalantis, our PMO (Project Management Office) is responsible for working with programs. The Yalantis PMO is a large structure that establishes organizational process assets (OPA) and shares with managers essential knowledge in the form of templates, best practices, and lessons learned.

      Each of the Yalantis program managers modernizes the processes in their program as needed. After that, audits on processes are conducted to improve skills. There is also a peer review at the level allowed by the NDA, enabling managers to adopt new best practices.


      The strategic role of the PgMO is vital, as adjusted sustained processes allow us to solve the needs of the client rapidly and efficiently.

      Program management is about focusing on delivering strategic benefits to your company. Hiring a program manager ultimately means taking care of your product’s success.

      Seeking a reliable management team or an experienced program manager?

      We can help you with improving the management of project interdependencies and the impact of the program on business goals.

      Contact us


      What frameworks do we use to manage a program?

      It depends on the specific program and client. We might use the SAFe framework, the Nexus framework, the Scrum@Scale framework, or some other framework. We do not focus heavily on a specific framework, but rather on how to properly adapt it to the client’s needs.

      What is unique about our approach to program management?

      Our approach is based on a hybrid of agile and classical techniques. We keep the feedback loops and quick releases of agile and deliver a predictable budget and schedule with the help of classical methodologies.

      What is the crucial difference between a project manager and a program manager?

      In short, a project manager is focused on current tasks, while a program manager sees the big picture and takes into account strategic goals and objectives.

      One of the most important steps in developing a successful digital software product is picking the right tech stack. Why? Because creating a product is not just about designing a nice user interface (UI) and a convenient user experience (UX); it’s also about designing a stable, secure, and maintainable product that will not only win your customer’s heart but will allow you to scale your business. Here’s where the right technology may help.

      While you, as a business owner, are busy with things like elaborating your business idea, defining your product’s pricing model, and coming up with powerful marketing, deciding on technologies for your new app is something you’ll likely leave up to your developers.

      Of course, it’s common practice to rely on your development partner’s technology suggestions. If you do, however, you should make sure your partner understands your business needs and takes into account all the important features you’ll be implementing for choosing a technology stack.

      At Yalantis, we believe that having a general understanding of the web development stack is a must for a client. It helps us to speak the same technical language and effectively reach your goals. This is why we’ve prepared this article.

      Without further ado, let’s explore what a technology stack is and what tools we use at Yalantis to build your web products.

      Defining the structure of your web project

      You might have guessed that a technology stack is a combination of software tools and programming languages that are used to bring your web or mobile app to life. Roughly speaking, web and mobile apps consist of a frontend and backend, which are the client-facing application and a hidden part that’s on the server, respectively.

      [A typical app tech stack]

      Each layer of the app is built atop the one below, forming a stack. This makes web stack technologies heavily dependent on each other. The image above shows the main building blocks of a typical technology stack; however, there may be other supporting elements involved. Let’s view the standard elements of frontend and backend development in detail.


      The front end is also known as the client side, as users see and interact with this part of an app. For a web app, this interaction is carried out in a web browser and is possible thanks to a number of programming tools. Client-facing web apps are usually built using a combination of JavaScript, HTML, and CSS. We’ll explain the components of the frontend technology stack below.

      Tools we use for frontend web development

      HTML (Hypertext Markup Language) is a programming language used for describing the structure of information presented on a web page. Yalantis uses the latest version of HTML — HTML5 — which has new elements and attributes for creating web apps more easily and effectively. The main advantage of HTML5 is that it has audio and video support, which wasn’t included in previous HTML versions.

      CSS (Cascading Style Sheets) is a style sheet language that describes the look and formatting of a document written in HTML. CSS is used for annotating text and embed tags in styled electronic documents.

      At Yalantis, we use CSS3 (the latest working version of CSS) along with HTML5. Unlike earlier versions of CSS, CSS3 supports responsive design, allowing website elements to respond differently when viewed on devices of different sizes. CSS3 is also split into lots of individual modules, both enhancing its functionality and making it simpler to work with. In addition, animations and 3D transformations work better in CSS3.

      JavaScript (or simply JS) is the third main technology for building the frontend of a web app. JavaScript is commonly used for creating dynamic and interactive web pages. In other words, it enables simple and complex web animations, which greatly contribute to a positive user experience. Check out our article on using web animations to create user-friendly apps for more on this topic. JavaScript is also actively used in many non-browser environments, including on web servers and in databases.

      TypeScript is a JavaScript superset that we often include in our frontend toolkit. TypeScript enables both a dynamic approach to programming and proper code structuring thanks to the use of type checking. This makes it a perfect fit for developing complex, multi-tier projects.

      Frontend frameworks

      Frontend frameworks are packages with prewritten, standardized code structured in files and folders. They provide developers with a foundation of pretested, functional code to build on along with the ability to change the final design. Frameworks help developers save time, as they don’t need to write every single line of code from scratch. Yalantis often chooses React for frontend web development, as this JavaScript library is perfect for building user interfaces. We also have mastered Angular and use it if a client prefers.


      Even though the backend performs offstage and is not visible to users, it’s the engine that drives your app and implements its logic. The web server, which is part of the backend, accepts requests from a browser, processes these requests according to a certain logic, turns to the database if needed, and sends back the relevant content. The backend consists of a database, a server app, and the server itself. Let’s look at each component of the backend technology stack in detail.

      Tools we use for backend web development

      Running on the server, the server app listens for requests, retrieves information from the database, and sends responses. Server apps can be written in different server-side languages depending on the project’s complexity. Yalantis uses such server-side programming languages as Golang, Rust, Ruby, and Node.js. These languages are versatile and boast a list of indisputable benefits.

      Golang is a statically typed programming language that allows for efficient code maintainability and management with a built-in package manager. The Go language is compiled and uses garbage collection to prevent memory leaks, ensuring a safe development process.

      Rust is also a statically typed language that takes the best from common rules of other statically typed languages such as Java and C++ and significantly improves those rules. Rust ensures efficient memory management without garbage collection or virtual machines (VMs), high speed and performance as well as helps to write relatively bug-free code. This programming language can be used in the development of large distributed systems, web services, IoT networks, and embedded systems.

      Ruby is an object-oriented programming language that provides good support for data validation, libraries for authentication and user management, and more. This language is easy to learn, flexible, and composable, meaning its parts can be combined and recombined in different variations. Ruby allows for quick web development with the help of the Ruby on Rails framework.

      Node.js is a JavaScript runtime environment. Node.js is commonly applied to the backend and full-cycle development. It has many ready-made solutions for nearly all development challenges, reducing the time for developing custom web applications. Read our detailed comparison of Node.js and Golang to familiarize yourself with the differences between them in terms of scalability, performance, error handling, and other criteria.

      Web frameworks

      Web frameworks greatly simplify backend development, and which you should choose depends on the programming languages you’ve picked. Any programming language has at least one universal framework. Libraries for a framework provide reusable bundles written in the language of the framework: for instance, code for a drop-down menu.

      However, frameworks aren’t just about the code: they’re completely layered workflow environments. Yalantis uses Ruby on Rails as a Ruby framework and Gorilla as a Golang framework. Both ensure clean syntax, rapid development, and stability.


      A database is an organized collection of information. Databases typically include aggregations of data records or files. For example, in e-commerce development, these records or files will be sales transactions, product catalogs and inventories, and customer profiles.

      There are many types of databases. In this article, we only touch upon databases Yalantis works with to outline some use cases for particular databases. Our specialists work with the following databases and choose which to use depending on the particularities of a client’s project.

      PostgreSQL. This database is especially suitable for financial software development, manufacturing, research, and scientific projects, as PostgreSQL has excellent analytical capabilities and boasts a powerful SQL engine, which makes processing large amounts of data easy and smooth.

      MySQL. Especially designed for web development, MySQL provides high performance and scalability. This database is the best fit for apps that rely heavily on multi-row transactions, such as a typical banking app. Generally speaking, MySQL is still a great choice for a wide range of apps.

      MongoDB. This database boasts numerous capabilities, such as a document-based data model. MongoDB is a great choice when it comes to calculating distances and figuring out geospatial information about customers, as this database has specific geospatial features. It’s also good as part of a technology stack for e-commerce, event, and gaming apps.

      Redis. Redis provides sub-millisecond response times, allowing millions of requests a second. This high speed is essential for real-time apps, including for advertising, healthcare, and IoT.

      Elasticsearch. This document-based data storage and retrieval tool is tailored to storing and rapidly retrieving information. Elasticsearch should be used for facilitating the user experience with quicker search results.

      Application Programming Interfaces

      An Application Programming Interface (API) provides a connection between the server and the client. APIs also help a server pull data from and transfer data to a database.

      [The web as a client–server app framework]

      Numerous services we use daily rely on a huge number of interconnected APIs. If even one of them fails, the service will not function. In order to avoid this, APIs should be thoroughly tested.

      Server architecture

      A developer needs a place to lay out written code from the early days of development. For this, we use a server setup.

      There are many variations of server architectures, including those in which the entire environment resides on a single server and those with a database management system (DBMS) separated from the rest of the environment. Your choice of server architecture should depend on such factors as performance, scalability, availability, reliability, cost, and ease of management.

      DevOps solutions for server setup

      Yalantis provides DevOps services for companies that run apps in the cloud to ensure the speed of development and operations. We use the services of cloud providers to host web applications. AWS is our priority for hosting web projects due to its flexibility, reliability, and security. As an alternative to AWS, we also use Google Cloud Platform Services, Microsoft Azure, and Heroku.

      A cloud provider gives us a server to use, and then our DevOps specialist:

      • sets up the environment — all additional software — to ensure smooth operation of the app. Our DevOps specialists typically use Nginx, which is a powerful web server. Nginx allows for setting up reverse proxies, load balancing, and more.
      • sets up a continuous integration/continuous deployment (CI/CD) pipeline from scratch. Continuous integration makes possible continuous development, adding code, and synchronous testing. Continuous deployment is responsible for delivering code to the server.

      We use such tools as GitLab and GitLab CI. GitLab is a single app for the whole DevOps lifecycle in which all written code is stored. We also use the GitLab CI (Continuous Integration) service, which creates and tests software whenever a developer pushes code to the repository.

      Having studied the basics of the technology stack, let’s move on to criteria that will help you and your development team select the most appropriate technologies for your project.

      Criteria that impact the choice of technology stack

      Keep in mind that the type of app you’re developing influences the technology you should select. A medical app, for example, will require high security, while audio/video streaming and file-sharing apps will need programming languages and frameworks such as Rust that can handle high loads.

      When deciding on your project’s technology stack, you should analyze your web app based on the criteria mentioned below to narrow down the options. Keep in mind that web development technologies can be used in different combinations, and frameworks usually are chosen after the programming language has been agreed on.

      However, sometimes the choice of the framework can impact the choice of language. For example, if you choose the Strapi open-source framework for developing a content management system (CMS) because it covers all critical CMS features, then it would be beneficial to use Node.js for the back end, as Strapi is built on Node.js.

      Before finalizing the tech stack for your project, you’ll need to share with your development team your business goals, all business requirements, as well as project constraints. Further, we dive into different project requirements and limitations.

      Functional requirements

      Functional requirements describe features that your web application should have. We need to differentiate among functional requirements that are the most impactful in terms of the software architecture and technologies, as there is a direct correlation between the software architecture and the technology stack. For instance, you may need your application to integrate with certain third-party services. To ensure it can, it’s important to maintain architectural flexibility in case any of these services change over time and it’s necessary to connect with a new service. For such an architecture, we’ll need to pick the technologies that fit the best.

      Functional requirements also help define the project’s complexity, which affects the choice of technology stack. As the size of a project grows, the complexity of the project usually increases too, as shown in the diagram below:

      [Relantionship between project complexity and size]

      Small projects. Single-page sites, portfolios, presentations, digital magazines, and other small web solutions can be implemented with the help of design tools like Readymag and Webflow. Ready-made solutions can be a better choice for small sites in terms of quick and cost-efficient development as well as simple site management for non-technical specialists.

      Medium-sized projects. Online stores, financial platforms, and enterprise apps require a more complex technology stack with several layers and a combination of languages, as these apps have more features and are developed with the help of frameworks.

      Large projects. Social networks and marketplaces are considered large projects and may require much more scalability, speed, and serviceability, which, in turn, require a versatile and well-suited tech stack.

      Nevertheless, deciding on a technology stack for a project of any size requires consideration of both functional and non-functional requirements.

      Non-functional requirements

      Non-functional requirements are also called quality attributes, and they resemble your expectations from your application, such as scalability, availability, a high level of security, high performance, or expanding to new markets. In particular, expanding to other countries may require choosing a tech stack that can ensure the same application functionality across regions (e.g. taking into account the availability of cloud providers).

      To ensure the high performance of your application yet save money, you may build the core of your application on a super-fast programming language like Rust and build the rest of the application in other, simpler programming languages. The Rust language is well-suited for developing highly performant software components with a low memory footprint.

      Quick time to market is also a non-functional requirement. If quick time to market is critical for you, we recommend using ready-made solutions that help to minimize the development and release time. For example, the Ruby on Rails framework, which provides access to a set of basic solutions, will save significant time. For Java, the Spring framework has lots of out-of-the-box solutions. Sticking to a popular technology will also save time in seeking out developers. And to top it all off, well-documented technologies facilitate the development of some features.

      If you expect your application to scale easily, that will also impact the technologies used. You can scale either vertically, adding additional resources for new tasks, or horizontally, adding processing units or physical machines to your cluster or database replica. Such languages as React, Rust, Node.js, Golang, and Ruby on Rails have great potential to ensure the scalability of your application. Your app will also scale well on AWS, as it uses advanced ethernet networking technology designed for scaling along with high availability and security.

      Your app may require high security. For instance, if you’re developing a health app, you should choose technologies that provide the highest level of security, especially if you operate with sensitive patient health information (PHI). Ruby on Rails, however, will be a good choice, as it provides DSL (Domain-Specific Language), helping you configure a content security policy for your app. Rust also adds to your application’s security by allowing for the development of practically bug-free software with a low chance of memory leaks.

      To provide security for the medical web app Healthfully, our specialists ensured the following:

      • All interactions with the app are carried out using an API.
      • Access to the API is token-based, with a time limit on token validity.
      • Access is granted for each specific request.
      • All infrastructure is in AWS, and access from the internet is possible only via the API; all other communication is behind a firewall.
      • Backups are performed regularly.
      • HIPAA compliance is ensured by securely sharing HL7 messages, limiting the visibility of sensitive data, and regulating the number of devices simultaneously logged into one account.
      [Components of the Healthfully app]


      Project constraints are indisputable and critical restrictions on the project. One of the most common constraints is the necessity for your application to comply with domain-specific or local laws and regulations such as Payment Card Industry Data Security Standard (PCI DSS) for mobile banking app development or the abovementioned HIPAA for US healthcare applications. If you work with an outsourcing development team, another project constraint could be the need to work with your in-house team to determine a common approach to the project. Constraints can’t be ignored or omitted. And as a rule, they can slow down the software development lifecycle.

      Choosing the technologies that best fit your web project isn’t easy, but there is a way for you to save time and effort. Find out what it is in the next section.

      Why is it best to work with a solution architect?

      As you can see, you need a substantial approach to choosing your project’s technology stack including a thorough examination of your goals and business requirements. Already at the initial project stage when you’re partnering with an IT consultancy company, you should start working with a competent solution architect to lay the foundation for your future project that will drive your choice of technology stack.

      A solution architect has all the necessary business domain and technical knowledge to help you decide on the right direction for your project. 

      Benefits of a solution architect for your project:

      • Ready architecture simplifies the choice of technology stack. A solution architect first develops an architecture, based on which it will be much easier to choose relevant technologies. Choosing technologies before designing an architecture can significantly slow down the development process.
      • Minimize mistakes from the very beginning. This results in future savings of time and resources that could be necessary for re-engineering the application.
      • Domain-specific knowledge allows your architect to define all project constraints you should take into account before deciding on a suitable technology set. Plus, your solution architect can tell whether your business requirements match your domain requirements.
      • Holistic understanding of technologies. Solution architects usually have broad knowledge about existing and emerging technologies and lots of ready-made solutions that can make projects in particular domains more time- and cost-efficient.

      As a result of your cooperation with a solution architect, you have an extremely high chance of getting a successful end solution that meets all functional and non-functional requirements right away.

      Steps not to lose a fortune developing a web app

      The following tips will help you be prepared for web app development so as not to have regrets about outsized expenses you could have avoided.

      Step 1. Make sure your specification is clear and understandable. If you outsource your project to an offshore web development team, make sure you have a clear project specification to help your developers prepare a precise estimate, which will allow you to plan your expenses. Any ambiguity will lead to a higher price. A detailed specification allows you to avoid this risk.

      Step 2. Create an MVP first and test it. Keep in mind that in certain cases, a landing page can serve as an excellent and inexpensive MVP. Make sure your product will be in demand, and consider all the errors that occur while testing. Only then should you develop a complete solution.

      Step 3. Use ready-made solutions when possible. Keep in mind that you don’t have to build all features from scratch, as similar solutions may already exist in the form of community-built libraries or third-party integrations (registration via Facebook or Google, for example). We’ve already mentioned that Ruby on Rails offers many libraries that accelerate web app and website development. ActiveAdmin is one of them. Using ActiveAdmin, developers can enable powerful content management functionality for their web apps.

      Step 4. Save on cloud hosting solutions. We use Amazon Web Services as the primary service for hosting web projects we create. AWS offers flexible pricing, with each service priced a la carte. This means you pay only for the services you use. This makes a lot of sense for server infrastructure, as traffic is unpredictable. This is especially true for startups, as it’s hard to say when exactly they’ll attract the first wave of users. That’s why the cloud pricing model suits startups best.

      Step 5. Think in advance. When selecting technologies for your web app, think about how you’ll support the app in the long run. Support will be easier if the app has a good architecture and optimized code from the very beginning. Any unsolved problems will appear later on and cause even worse problems. Support and maintenance should be considered when choosing the tech stack, as their consideration will simplify updates even if you decide to change software development service providers.

      These are the most widely discussed web application development trends in 2023. Artificial intelligence is gaining ground, as it offers a personalized user experience by gathering users’ data and speeds up interactions with a web application. Another trend that’s worth mentioning is progressive web apps (PWAs), which provide a mobile-like experience and can be easily installed via a shareable link.

      Different web apps use different development tools. This is the best evidence that there’s no single most effective technology stack. When choosing a technology stack for web application development, keep in mind the specifics of your project. Solution architects at our outsourcing company will consider your product requirements and turn them into an architecture and design that lay the foundation for a top-notch solution. Our agency can help you with this if you share your web app idea and expectations with us. Tell us what you want to achieve, and our technical experts will gladly suggest the best tools to make it happen.

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      Why is it important for a business owner to take part in choosing the technology stack for a web project?

      Business owners should be in the loop on technologies that are used for their projects in order to stay on the same page as developers and understand what exactly they’re investing in. If you know the specifics of common programming languages and frameworks, your development team won’t have to spend lots of time explaining and justifying their every decision, freeing more time for actual development.

      Why is developing an MVP first a reasonable idea?

      Beginning with an MVP rather than a full-fledged web application is first of all time- and cost-efficient. You’ll be able to see the first results quickly and at a reasonable cost. By evaluating the first version of your solution, you’ll know what to improve in the next iterations to launch the best web system possible.

      How flexible is the web development process if project requirements change?

      Businesses don’t stand still, and it may be that during project execution you realize that your project needs changes. It’s best if you know exactly what you need from the get-go. But if any changes are necessary, we surely can modify our development process and adjust it to your new needs. You should remember, however, that such abrupt changes may require additional investment, time, and technical specialists.

      Increased demand for investment and stock market application development has been driven by the evolution of user preferences: more and more people prefer to manage their money using tablets and smartphones. Robo-advisors and stock trading apps have embraced this transition. Let’s look at some facts:

      • Statista reports that 56 percent of Americans in 2021 considered stocks the best long-term investment option.
      • Deloitte survey says that in 2021, the investment management industry fared well despite pandemic-driven market volatility.
      • According to a Research And Markets forecast, the global WealthTech solutions sector is predicted to grow to $137.44 million in revenue by 2028, increasing at a CAGR of 14.1 percent between 2021 and 2028.

      These statistics illustrate that there’s a place in the market for your cutting-edge mobile product or digitalized investment advisor platform.

      Based on our in-depth research and solid expertise, we’ve created this article to help you develop a competitive and trendy investment management platform. Our post will be insightful for:

      • investment management companies that need to digitize their customer service
      • startups that want to build a consumer platform for self-service investment management
      • market players that want to create a SaaS investment management platform tailored to the target audience mentioned in the first bullet.

      If you have questions about how to build a stock trading app, keep reading. In this article, we will shed light on the crucial aspects of developing apps for investing money. But first, let’s consider your target audience.

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      Who is the target audience of your investment management platform?

      It would be a mistake to think that money management platforms and investment tracker applications are relevant only to older adults. In 2022, younger investors — Gen Z respondents (ages 18–25) and Millennials (ages 26–41) — were more likely than others to make active moves in their investment accounts according to a study conducted for Bankrate by YouGov. Find out how we integrated digital banking services into a wealth management platform to target Gen Z and Millennials.

      As the numbers show, Generation X (ages 42–57) and Baby Boomers (ages 58–76) were apt to do nothing in response to inflation and market volatility in 2022. At the same time, respondents aged 45 and older are users of personal investment apps. That means they should also be counted as the target audience, even if they’re less active. 

      In building a brokerage app or investment platform or during any other financial software development services, Yalantis experts recommend considering the needs of different age groups — Generation Z, Millennials, Generation X, and Baby Boomers — with a slight focus on the younger generations. 

      Today, when interacting with an investment platform, all users expect an easy-to-use application that offers transparency and an intuitive interface without information overload. But there are still some generational differences in expectations. For younger users, simple functionality and the opportunity to make small investments without paying high commissions will be important. Whereas for older users, it’s necessary to have a lot of charts to help them make informed decisions, as well as educational modules so they can research companies they’re interested in.

      Later, we describe how to implement such functionality in your investment app in more detail. For now, let’s pay attention to the latest technologies that are worth considering in 2023.

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      Emerging technologies to consider adding to an investment platform

      The market dictates the need for digitalization, which, in turn, gives rise to a certain number of startups. FinTech startups using emerging technologies in all sectors — from digital payments to InsurTech, mobile banking, and cross-border payments — are riding the strong focus of investors

      An existing surge in startup investment led to an increase in the number of FinTech unicorns back in 2021. A unicorn is a privately held startup company valued at more than $1 billion. According to CB Insights, as of July 2022, there were 1,100 unicorns worldwide. A lot of them are in the FinTech sector.

      Deloitte survey responses show that the most important drivers of digitalization with emerging tech in investment management are improving operational efficiency (45 percent) and creating opportunities that did not previously exist or were not viable (42 percent).

      Emerging tech to improve operational efficiency

      For traditional investment managers, operational efficiency is becoming increasingly important as the rise of low-cost, passive mobile app investing increases competitive pressures. For private equity firms, effectively sourcing deals and improving portfolio company operations are high priorities as deal values rise. Therefore, companies are increasingly turning to digital technologies to improve operations. 

      A Deloitte survey also shows that all geographies are expecting a bigger increase in net spending across emerging technologies compared to last year.

      For now, it’s worth focusing your attention on the following technologies:

      • Cloud computing and storage to support agile operations and increase operational flexibility and efficiency 
      • Cybersecurity solutions to help companies safely adopt new operating models
      • AI and RPA to implement a greater degree of process automation and workflow optimization. These technologies can help reduce development costs and time. For example, Invesco used intelligent automation to save 3 million minutes per year on 35 business functions spanning the front and back offices. Implementing these technologies resulted in annual cost savings of about $2.1 million.

      Adopting new technologies can also help generate alpha and better serve customers.

      Emerging tech to create new opportunities

      It’s also important to mention natural language processing and generation (NLP/G), a type of AI technology, as it helps to summarize structured and unstructured data from diverse sources on investment management platforms. This technology also assists you in reducing the time spent collecting data, allowing you to focus instead on analyzing data with greater potential for insights.

      The majority of Deloitte survey respondents using AI solutions in the pre-investment phase agreed that AI helps them generate alpha. In addition, there was a strong correlation between the ability to generate alpha and increased employee engagement, productivity, well-being, and responsiveness. The combination of these factors is also likely to lead to an increase in alpha generation revenue in 2022. 

      Due to emerging tech, investment managers can also engage with clients in a new way. Companies tend to create personalized customer interactions using data analytics by identifying customer interests, preferred content formats, and frequency of interaction. Personalized communications help customers get the information they’re looking for. This capability, in turn, can enable advisors to close deals faster. Because investment management is largely a relationship-driven business, companies that can better engage with clients and meet their expectations can become more successful. 

      Regardless of what you aim to achieve on your platform with the latest technology, you may encounter problems. Let’s discuss a few issues that are worth your attention.

      Implementing the latest technology for an investment platform: things to consider 

      Most Deloitte survey responses faced challenges in implementing AI (67 percent), cybersecurity (58 percent), and cloud computing (54 percent). Generally, there are two reasons:

      • The complexity of implementation is the top barrier to implementing AI (29 percent) and cloud technologies (31 percent).
      • Cybersecurity implementation is stifled by reliance on legacy systems (16 percent).

      To make the most of technologies like cloud computing, cybersecurity solutions, AI, and RPA, you need to build the right data infrastructure. For example, as data is the building block of AI models, you may need to implement a data warehouse that aggregates and stores clean data. 

      Similarly, an effective RPA implementation may require robust exception handling to ensure that systems perform as expected. Intelligent automation that uses AI and RPA, either separately or in combination, can allow you to cut costs, increase revenue by being more precisely targeted to customers, and execute processes two to three times faster than humans can without these advanced technologies.

      More considerations when developing a trading app and an investment platform

      There are many important factors beyond implementing the latest technologies that are essential to consider during investment platform development, payment app development, or when improving an existing investment product.

      Expand functionality to gain a competitive advantage when developing a stock trading app 

      To be trustworthy, investment platforms or apps have to be transparent about their fee structure and need to have customer support and educational resources. Advanced functions that can appeal to even more users include the following:

      • Charting useful capabilities. When line, candlestick, and volume charts are available in a mobile money investment app, investors can get information and tools to make informed investment decisions.
      • Educational content on how to invest safely (like online webinars, financial calculators, and newsletters).
      • Automated counselors. Integrating robo-advisors (automated online investment assistants) into your robo investing app and FinTech software is a top priority. Users would gladly take personalized advice from a computer algorithm if their stock market investment apps offered it.
      • Wide range of assets. The more investment assets are supported, the the more users will find your app attractive. You can try to expand your line of stocks, ETFs, and other funds in an investing app.
      • Offering spare-change investing or round-up investments will allow users to invest small amounts in specific stocks. Investing apps typically connect to a debit or credit card of users, so they round up purchases to the nearest dollar and automatically invest the difference. For example, if a user spends $3.75 on a cup of coffee, $0.25 will be put toward a chosen stock.

      You also can consider implementing the following in your investment platform to enhance efficiency in the use of financial services and increase customer retention through speed and convenience:

      • Aggregating customer financial data
      • Managing financial goals
      • Providing real-time reports
      • Functionality for opening/monitoring investment accounts
      • Routine automation for operational efficiency
      • Customer portals for optimized collaboration. Users can do simple operations on their own without the need for a consultant.

      Once the investment management platform functionality has been properly chosen for your particular case, it’s worth thinking about the next important step: design.

      Ensure a sophisticated design for your investment platform and trading application

      Ensure a sophisticated design for your investment platform and trading application

      Consumers are spoiled for choice, which is why hyper-personalized solutions and deeper insights on investments allow you to stay competitive in the market.

      Your users should find financial guidance, account aggregation, goal setting, investing, and banking options to be both user-friendly and seamless to navigate. Let’s start with key points about the user experience.

      1. Meaningful UX. When thinking about what an investment platform should look like to help users, it’s worth taking into account the needs of the vast majority. In other words, when creating your platform, we recommend creating something in between the minimal functionality of simple stock investing apps and the complex functionality of heavy trading platforms like Binance. A mobile app should satisfy both those users who enter the app once every two weeks to buy a stock for $10 and active investors who pay attention to daily market noise and buy and sell for large sums. An example of such an application is Viewtrade mobile app. 

      For the desktop platform, intuitiveness will be an integral part of success. You can compare Orbis Pro Trader, a platform that is visually overloaded, with the modern Lifeworks platform.

      To be in demand on the market in 2023, both platforms and apps should have a smooth UX so users can easily understand how to navigate. It’s extremely important to not let your stock investment application become a case of information overload that turns off users who are not as enthused about stock trading as their heavily invested counterparts.

      If users don’t understand how to make a trade, don’t know where to find educational information, or don’t understand the charts, it’s unlikely they will continue to invest or work with your product, as there are also huge sums at stake.

      2. Simple UI. During investment app development, you should implement a conversational user interface (CUI). A CUI will help you connect with users simply and intuitively to provide a realistic feel while interacting with the system, increasing user attention. In investment platforms, Yalantis experts use conversational user interfaces that leverage NLP. This allows the system to understand, analyze, and create meaning from human language data structures.

      In addition to the fact that design is an essential part of a competitive modern app for investment, there are several complexities that exist beyond design.

      Consider investment platform and stock market app development challenges

      The first challenge to consider when it comes to apps for investing is cybersecurity.

      Cyber threats

      Data breaches are unacceptable because investment platforms are quite sensitive and contain confidential information about users, such as social security numbers. 

      Here are some steps that Yalantis’ experts follow to ensure the complete security of your application:

      1. Two-factor authentication. This helps protect users’ accounts by requiring two sources of authentication to sign in: something users know (such as a password) and something users have (such as a one-time verification code or a device approval request). Every time a user logs in or makes changes to the account, they will be required to verify their identity before completing the action. This helps to protect the account even if someone knows or guesses the password.
      2. Encryption. Sensitive information, such as social security numbers, should be encrypted before it’s stored. In addition, your platform can securely communicate with your servers using the Transport Layer Security (TLS) protocol with up-to-date configurations and ciphers. TLS helps to ensure that anything users send to your servers remains private — including personal and account information such as passwords and bank account credentials.
      3. Password safety. Our recommendation is to hash user account passwords using the industry-standard BCrypt hashing algorithm and never store them in plaintext. This means they will be stored in an encrypted format, making them harder and more time-consuming for attackers to crack.
      4. Perform real-time threat analysis and fraud prevention based on AI and ML tools.
      5. Keep application programming interfaces (APIs) safe. The following practices may be helpful: cataloging all available APIs, identifying potential attack vectors based on the API’s function, and restricting API output to only expose the minimum necessary data.

      If you implement these practices and keep your security policies up to date, you will provide a high level of security for your product.

      Laws to keep users’ money safe

      The financial laws of each state vary, with implications concerning the legal status of the investment stock app providing financial services, the presence and size of the authorized capital, licenses, taxation, and reporting requirements. 

      Ignorance of important legal requirements and inadequate reporting on which digital transformation takes place can lead to significant financial, legal, and reputational consequences. Firms that are putting governance and control mechanisms in place as they go along their transformation journey are likely to have the upper hand when financial measures become mandatory compliance requirements.

      What we recommend focusing on:

      1. If your business is located in Europe, you must comply with the General Data Protection Regulation (GDPR).
      2. If you want to work in the EU market, you have to fulfill the requirements of the PSD2 Directive. You’ll have to put up with a thorough review of all the data you provide and follow rules to protect consumers’ rights to payment services.
      3. In the US, you need to take into account the Securities and Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA). They regulate brokerages and stock investing apps. Both SEC- and FINRA-registered apps for stock investing have to meet certain requirements.
      4. If you need to track your automatic investing app, you can do so by searching for your firm at or In addition, most trustworthy brokerages and automated advisors offer insurance with the Securities Investor Protection Corporation (SIPC). This is a nonprofit membership corporation that protects money invested in a brokerage that files for bankruptcy or encounters other financial difficulties.

      Finally, yet importantly, let’s talk about challenges with payment for order flow (PFOF) transparency.

      Transparency around payment for order flow

      PFOF statistics are usually published by brokers. They’re the best way for investors to ensure accurate and timely trade executions. PFOF is going to be important when evaluating online brokers going forward because most online brokers have joined the no-fee movement. According to the SEC, payment for order flow is a method of transferring some of the trading profits from market making to the brokers routing the orders.

      Why is it important to comply with the PFOF method? The popular investment tracking app Robinhood paid out $65 million to settle the charges of The Securities and Exchange Commission as a result of PFOF. According to SEC estimates, Robinhood’s poor order execution cost its customers $34.1 million from 2016 to 2019. Robinhood also seemed to hide the fact that PFOF was its primary way of making money.

      By taking all of the abovementioned factors into account when developing your product, you can avoid a variety of turbulent stages. 

      Yalantis insights on building an investment management platform for business growth

      Based on our extensive experience in investment advisory platform development, we will highlight a few things that can help you improve your platform.

      Mitigate conduct risks

      In addition to the latest technologies we mentioned at the beginning of the article, technological solutions that can help mitigate a heightened level of conduct risk (a form of risk that refers to potential misconduct of individuals associated with a firm, including employees, third-party vendors, etc,) include:

      • enhancement of personal account monitoring 
      • prioritization of higher-risk events 
      • data analytics on alert metadata
      • restrictions on the use of high-risk communications systems.

      Furthermore, establishing appropriate intelligent third-party risk management and real-time monitoring systems and verification of third-party responsible operation claims can help mitigate the third-party risk.

      Choose a digitization strategy

      A firm’s core technology infrastructure generally has a significant bearing on its digitization strategy. Depending on what you’re starting to develop the platform with, there are several options:

      • Firms with modernized core infrastructures can increase their in-house technology spending on advanced technologies to enhance their investment decision-making process and client service. 
      • Less digitized firms can turn to third-party cloud-based asset servicing platforms to get a unified view of client data across the extended organization. Such an approach allows firms to rapidly derive insights from data and develop products faster while mitigating some of the risks associated with substantial technology development projects.
      • Those with legacy infrastructure may have to adopt a different approach to efficiently use advanced technologies and data analytics. So it might be worth performing a legacy code audit first, then applying a better-suited technology stack.

      All of these aspects and many others the Yalantis team takes into account when developing a custom investment management app or platform. To determine where to start with development or how to improve it, it’s worth consulting with an appropriate provider, who you should primarily choose before starting the development.

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      Demand for mobile peer-to-peer (P2P) payments is on the rise. Such payments are the most popular way of transferring money online. During the COVID-19 pandemic, people around the globe preferred non-cash transactions as part of efforts to reduce transmission of the coronavirus. Thus, consumers had an opportunity to appreciate the advantages of P2P payment apps. The numbers speak for themselves:

      • According to Allied Market Research, the size of the international P2P payments market was estimated at $1,889.16 billion in 2020 and is predicted to reach $9,097.06 billion by 2030, increasing at a CAGR of 17.3 percent from 2021 to 2030.
      • 42 percent of respondents participating in the Statista Global Consumer Survey 2022 said that they sent money to acquaintances in the past 12 months by means of a direct fund transfer service like PayPal.
      • states that in 2021, Cash App subscription- and services-based revenue was $1.89 billion, a 63 percent rise compared to 2020. Cash App’s competitors such as Venmo and Zelle have also experienced a jump in demand for their services.

      These statistics demonstrate the prospects of growing your P2P payment business with a well-thought-out mobile application. No wonder the query of how to create a mobile peer-to-peer payment app is popular among banking and fintech companies. This post is on how to develop a payment app and will be of interest for banking, financial services, and insurance market players, including:

      • Banks that are building P2P payment systems to provide customers with accessible and convenient banking services. Thus, customers will be able to effortlessly transfer money and pay for multiple services (utilities, phone bills, insurance, fines, and more).
      • FinTech companies that want to start a P2P payment app development from scratch or improve an existing solution.

      Based on thorough research and our FinTech solution development expertise, we’ve compiled a list of ingredients so you know how to build a P2P payment app based on the example of three popular P2P apps: Zelle, Venmo, and Cash App. In order for you to know how to build a payment app, let’s get down to viewing these ingredients without delay.

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      Common cybersecurity vulnerabilities, incidents, and approaches to ensuring top-notch security during online payment transfer app development

      According to Allied Market Research, the acceleration of data breaches and security issues in P2P payments are predicted to impede market growth in the near future. Users of P2P apps often complain of money vanishing without explanation and having a hard time getting a refund after a mistaken payment. Venmo, Zelle, and Cash App have been criticized by security experts for having serious and unresolved privacy issues. Let’s check out some of these cybersecurity vulnerabilities and related incidents and find measures that could prevent them.

      Employee-caused data breaches

      In December 2021, sensitive data for over eight million users of the Cash App Investing platform was disclosed when an employee downloaded corporate reports after quitting the company. Exposed data included the value of some clients’ portfolios and particulars of their stock trades. To avoid such incidents, you can suppress external access to staff accounts and disable access as soon as a specialist quits.

      Fraudulent activity 

      Fraud has become a serious issue for Zelle, Venmo, and Cash App. For example, a frequent Zelle scam involves scammers introducing themselves as bank fraud investigators and then persuading users to make payments to them. Zelle has taken steps to address its fraud problem: in April 2022, it launched Authentify, an identity verification service. One more step to fight scams on your platform is introducing a vigorous fraud detection and mitigation procedure.

      Publicly available user data

      Venmo payments are public by default, since Venmo is both a payment app and a social network. The transparency of the app’s transactions and other user information has been called out by privacy experts for years. One of the most recent and highly discussed examples of where this transparency can play a nasty trick is an incident that happened in May 2021. It took Buzzfeed journalists 10 minutes of searching in Venmo to discover President Joe Biden’s, his family members’, and his friends’ Venmo accounts.

      In response to the Buzzfeed report, the money transfer app implemented new privacy controls enabling users to make their list of contacts private. A bit later, the company gave up the global feed previously displaying users’ payments in real time. Currently, the app’s social network components are limited to users’ friends in the “friends feed”.

      Experience has proven that when it comes to cybersecurity, P2P apps are likely not foolproof. We urge you to create a payment app implementing the following measures to minimize the possibility of cybersecurity incidents:

      • Create a Cash App-like application compliant with PCI requirements from the start. PCI DSS (Payment Card Industry Data Security Standard) certification is a requirement for any company that processes credit or debit card transactions.
      • Use encryption to protect user account data and track users’ account activity to identify unauthorized transactions.
      • Avoid one-click transactions and instead ask users to double-check payment details prior to hitting send.
      • Use artificial intelligence tools to reveal irregular user behaviors that could indicate fraud.
      • Inform users by means such as pop-up alerts about the risks associated with peer-to-peer payment transactions to persuade them that they should avoid transferring funds to unfamiliar parties.

      In addition to the above-mentioned, you can implement multi-factor authentication (MFA). This will guarantee that hijacked password and username combinations alone won’t be enough to give intruders access to user accounts. Money transfer apps adopting MFA request that users provide at least one extra factor of authentication to confirm their identities.

      Despite periodic fraudulent activity involving Zelle users, one of the app’s key selling points is the common opinion that it’s more secure than Venmo and Cash App. Zelle was launched by dominant banks, while Venmo and Cash App are independently operated companies. So what governed the success of Venmo and Cash App? Mainly, it’s their great user engagement strategies.

      Security best practices for web and mobile app development

      Read the post

      How to make a payment app user-engaging

      It’s no surprise that most individuals don’t consider financial management a hobby. Managing your savings can be tedious if you aren’t financially literate or don’t have access to easy-to-use and engaging financial tools. That’s why during mobile payment app development you should prioritize intuitive and convenient user experience flow (we’ll discuss how to ensure this a bit later) and have proper user engagement components in place.

      The rate of user engagement indicates the frequency and duration of interactions with your app. This metric allows you to determine if users find value in your services. Businesses measure engagement by monitoring user actions including clicks, downloads, and shares, and then analyze this data. The more people engage with your app, the more it becomes a matter of habit for them. Let’s view how Cash App and Venmo succeeded in ensuring high rates of user engagement.

      Cash App’s user engagement strategy

      The key to the colossal success of Cash App is its one-of-a-kind viral and influencer marketing strategy.

      Cash App Friday campaign. Taking place on Twitter and Instagram, Cash App Friday is a unique social money giveaway that starts on Fridays. The particular Fridays are chosen at random by the company. Cash App Friday may happen several times a month or not once. To participate in the promotion campaign, users need to follow the company on social networks so as not to miss the next Cash App Friday. To win the giveaway, users have to like, share, and leave a comment on a post in addition to sharing their $Cashtag identifier (a unique identifier for people and businesses that use Cash App). Such posts get hundreds of thousands of retweets and comments.

      Collaboration with brands and celebrities. Cash App has partnered with brands for charitable causes and to hold raffles. One instance is a joint campaign with Burger King – the companies offered to pay off the student debt of chosen Twitter users. Campaign-related posts on Twitter gained 89,000 retweets and 40,000 likes. Moreover, Cash App ran analogous promotional campaigns collaborating with stars including Travis Scott and Lil B. Such partnerships helped the company advertise the app among these celebrities’ fans.

      Educational content for improved financial literacy. To attract Generation Z to the app, Cash App provides content streams aiming to enhance users’ financial literacy. This content is presented in hip hop aesthetics. “Cash App Wisdom” is a series of explanations devoted to financial topics including the basics of investment and account protection. Various classes on money management are conducted by Meghan Thee Stallion, an American rapper. Content delivered in collaboration with Red Bull Racing Honda helps teach users about crypto operations. Such educational content results in an enormous following and distinguishes Cash App from other money management apps.

      Venmo’s user engagement strategies

      What has contributed to the tremendous success of Venmo is that it’s a payment app with rewarding components and a social network all in one.

      Social network component. Over 90 percent of all the app’s transactions are visible to a user’s acquaintances or network and receive reactions in the form of emoji or comments. If someone wants to know what their friend is doing or recently did, they can go to the Venmo feed. This social media element not only makes Venmo alluring among younger users but also contributes to great user engagement. Users open Venmo several times a week just to check what their friends are doing.

      Venmo rewards program. Venmo credit card holders receive cashback on all purchases, which instantly goes to the app’s balance. Throughout all statement periods, users automatically earn three percent on their top spending category, two percent on their second spending category, and one percent on all other eligible purchases. The eight spending categories are trackable by means of the Venmo app. They include grocery, bills and utilities, health and beauty, gas, entertainment, dining and nightlife, transportation, and travel.

      It’s obvious that even thoroughly thought-out user engagement strategies won’t be effective if your Cash App clone’s user experience is poor. Let’s check what makes the most popular P2P apps user-friendly.


      Ensuring a smooth user experience during P2P payment app development

      The UI and UX of your money transfer app should be alluring, intuitive, and simple. Keep in mind that consumers aren’t willing to spend much time figuring out how everything works. In order to create a payment app that will be appreciated by users, make your UI/UX as clear and minimalist as possible.

      Make Cash App features built by Cash App developers and its user experience. The app has a modern and user-friendly interface. The Cash App application brilliantly uses bold colors and large text to provide users with a fresh and dynamic experience. Here are some Cash App design ideas you can get inspired by.

      • The app’s onboarding process is easy and fast. Users receive a verification code, enter it, and provide personal information including their name, zip code, and debit card details. Then the app asks users to make a “$Cashtag,” a name used by those who want to send them money. The entire onboarding procedure is performed through several bold green screens where a user enters the required details.
      • The app’s further use. The default home screen encourages a user to insert a sum of money and choose either “request” or “pay” to transfer money or get a payment. You can find a person by name, $Cashtag, email, and SMS. Moreover, you can use Bluetooth to locate users if they are in close proximity. The convenient account screen lets you see your personal data and change preferences, including enabling auto cash out or security lock. There’s also a private activity feed allowing only you to see your payment history.

      Why and how to redesign your existing P2P app

      If you already have a fully functioning P2P payment app, you might consider custom financial software development to revamp and redesign it for several reasons:

      • Low rating on the app stores (three or fewer stars, or lower than your usual rating)
      • Numerous and frequent negative user reviews
      • Specific issues that users notify you about in their reviews
      • Negative feedback on social media
      • Frequent security issues
      • Expanding the set of services provided by your app

      In 2021, Venmo announced a redesigned app. The redesign was in response to the need for providing improved security and attracting attention to the app’s broader set of services beyond P2P payments. Updates included:

      • Improved social feed. As we mentioned above, the company gave up its global feed. The friends feed is currently the only social feed available in the app.
      • Bottom navigation. Now users can smoothly switch between their social feed, various features (operations with credit/debit cards, recently added cryptocurrencies, etc.), and their personal profile. Before the redesign, users had to comb through a menu to find services not related to peer-to-peer payments.
      • Cards button. Venmo credit and debit card holders can access controls and functionality to use and manage their cards, rewards, and offers in an easily accessible place.
      • Crypto button. Users can easily delve into the world of cryptocurrencies, analyze trends in real time, and purchase, sell, or keep four types of cryptocurrencies right in the app.
      • Enhanced personal feed. Users now have a holistic view of their wallet, activity, and settings right in their personal feed. Users see their balance, view the history of transactions, and manage and monitor expenditures in a central place.

      Regardless of the appearance of new functionality and new ways to manage your money, the app remains easy to use and preserves its entertaining social network components. Users can still see other users’ transactions and leave emojis to demonstrate what payments are for.

      To effectively redesign your app as Venmo did, conduct user interviews by asking users the following questions:

      • When, where, and how do you access the app?
      • What is your purpose for using the app?
      • How fast are you able to achieve your goal with the app?
      • What do you love and hate about the app?
      • What else would you like to be able to do with the help of the app?

      Collecting such user feedback will help you figure out what users think about the app with precision and determine exactly where and how the app can be enhanced.

      More insights on how to create a P2P money transfer app and run a successful P2P payment business

      These are all important factors to be considered when planning to start P2P payment app development, improve it, or while growing your paper-to-peer payment business.

      Define your competitive advantage 

      The experience of leading P2P payment app development proves that differentiation is likely to be a key determinant of your app’s continued growth. For example, people consider Zelle as the best service for instant transfers. Venmo is surely best for groups of friends. Cash App is regarded as best for investors. In its turn, PayPal, the oldest P2P player, keeps holding its own in ecommerce. Lastly, Google Pay is preferred by people using digital wallets.

      Wisely adopt proven business strategies 

      Emerging P2P apps often successfully follow the example of popular services with proven business strategies. Take London-based VibePay, for example, which was inspired by Venmo’s social network component. As with Venmo in the US, VibePay is striving to target Generation Z and Millennials in the UK by means of its social media-like functionality that enables users to leave a message or an emoji about each transaction.

      Expand functionality beyond peer-to-peer payments

      Cash App has expanded its services beyond P2P payments. Consumers can receive direct deposit and Automated Clearing House (ACH) payments in addition to buying cryptocurrency and trading stocks.

      For the record, Cash App was among the first popular P2P apps to offer users cryptocurrency operations. Block, the company behind Cash App, began enabling users to purchase, sell, and hold Bitcoin in 2018. In comparison, PayPal launched a similar service only in 2020. Cash App keeps making it easier to invest in Bitcoin. The company has recently introduced new Paid in Bitcoin, Bitcoin Roundups, and Lightning Network Receives services.

      To date, over 10 million individuals have purchased Bitcoin using the app, as stated in Block’s Q1 2022 shareholder letter. In the first quarter of 2022, Cash App generated $624 million in gross profit, $43 million of which was gained from Bitcoin sales.

      Expand your target audience to the business segment

      Having a Venmo business account lets businesses get payments from their clients online or in person using a unique QR code. A business can create a business profile free of charge. However, there is a 1.9 percent plus $0.10 fee per transaction. Consumers can transfer money to businesses the same way they do when sending money to their acquaintances. The Venmo app allows for one-tap switching between personal and business profiles.

      In terms of business transactions, Zelle keeps up with Venmo. To achieve its growth in the business segment, Zelle has also introduced a QR code function that enables consumers to effortlessly pay businesses. Albert Ko, CEO of Zelle, says that the majority of the app’s transactions are peer to peer, but businesses are increasingly using the app. He states that in 2021, payments made via the Zelle app for small businesses grew 162 percent over 2020 totals.

      Adopt technologies that simplify the payment process

      The COVID-19 pandemic spurred demand for mobile banking app development with NFC (near-field communication) payments and QR codes. In response, PayPal, the owner of Venmo, decided to take advantage of both trends for the app’s first credit card. Now Venmo users can receive a credit card that combines a contactless payment chip and a printed QR code. By using this card, consumers can get peer-to-peer payments and split bills with others. In addition to the physical credit card, consumers obtain a virtual card used for online shopping even when the physical card is frozen due to loss or theft.


      Don’t know how to make a payment app? The quality of your app and its subsequent success primarily depends on the expertise of your payment app development partner, so take your time to choose. Yalantis experts have experience building digital financial solutions. We help financial institutions and FinTech companies build sophisticated and engaging software products. We will gladly help you implement your idea.

      Want to create custom payment software?

      We can help

      Contact us

      The way you manage data in your application plays a crucial role in delivering a positive user experience. At the end of the day, it doesn’t matter how well your app’s interface is designed and how clean your code is unless your application is capable of quickly retrieving, processing, and delivering data. Moreover, all of this data should be protected so that intruders can’t get their hands on it. Luckily, this can be achieved with a wisely chosen database management system.

      A database is a place where you store and organize all the data you collect through your app, while a database management system (DBMS) is software for conveniently managing this database. Our clients often ask: What database should I use?

      There are more than 300 databases on the market. Choosing between so many tools is overwhelming. But the nice thing is that you don’t have to. We’ve done the hard work for you and will share our findings. In this article, we give you valuable tips on how to choose a database for your software solution. And if you’re still not sure when to use databases and whether you even need them, we can help you too.

      6 questions to ask yourself when choosing a database

      Here is a list of questions you should ask yourself when deciding which database to choose:

      1. How many people will use my application simultaneously?
      2. What is my bigger preference: data security or application performance?
      3. What are my other critical non-functional and business requirements?
      4. Do I plan to scale my database in the future?
      5. Do I want to analyze my data or implement any advanced technologies in my application like machine learning and artificial intelligence (AI)?
      6. Do I need to integrate my database with other solutions like business intelligence tools?

      This isn’t a complete list of questions that can guide your whole database selection process, but they’re enough to set you in the right direction in finding the best database to use. First, answer these questions by yourself. Then read this article further for more detailed answers and to make the final decision on which database to use.

      SQL vs NoSQL database

      When it comes to choosing the best database solution, one of the biggest challenges is picking between an SQL (relational) and NoSQL (non-relational) data structure. While both have good performance, there are key differences you should keep in mind.

      SQL databases

      A relational database is a set of tables that have predefined relationships between them. It’s the most used type of database. To maintain and query a relational database, the database management system uses Structured Query Language (SQL), a common user application that provides an easy programming interface for database interactions.

      Relational databases consist of rows called tuples and columns called attributes. Tuples in a table share the same attributes.

      Advantages of SQL databases

      A relational database is ideal for storing structured data (zip codes, credit card numbers, dates, ID numbers). SQL is a mature technology that:

      • is well-documented
      • boasts great support
      • works well with most modern frameworks and libraries

      The best SQL databases are PostgreSQL and MySQL. Both have proven stable and secure.

      Another great advantage of relational databases is their security. The best relational databases support access permissions, which define who is allowed to read and edit the data. A database administrator can grant particular user privileges to access, select, insert, or delete data. This gives no chance for third parties to steal information.

      Using the best relational database management system (RDBMS) protects against data loss and data corruption thanks to compliance with ACID properties: atomicity, consistency, isolation, and durability. To better understand what this means, let’s assume that two buyers are trying to simultaneously purchase a red dress of the same size. ACID compliance ensures that these transactions won’t overlap each other.

      • Atomicity means that each transaction (a sequence of one or more SQL operations) is treated as a unit. It can either fail completely or succeed completely, and if one of the operations fails, the whole transaction fails. When a user purchases an item, money is withdrawn from the user’s account and deposited to the merchant’s account. Atomicity ensures that if the deposit transaction fails, the withdrawal operation won’t take place.
      • Consistency means that only valid data that follows all rules can be written in the database. If input data is invalid, the database returns to its state before the transaction. This ensures that illegal transactions can’t corrupt the database.
      • Isolation means that unfinished transactions remain isolated. It ensures that all transactions are processed securely and independently.
      • Durability means that the data is saved by the system even if the transaction fails. Thanks to durability, data won’t be lost even if the system crashes.

      ACID compliance is beneficial for apps handling sensitive financial, healthcare, and personal data, since it automatically provides safety and privacy to users. Thanks to all these advantages, relational databases are a perfect fit for financial and healthcare projects.

      Disadvantages of relational databases

      But relational databases have disadvantages as well:

      • Lack of flexibility. Relational databases don’t work efficiently with semi-structured or unstructured data, so they aren’t a good fit for large loads and IoT analytics.
      • When the data structure becomes complex, it becomes harder to share information from one large data-driven software solution to another. At big institutions, relational databases often grow independently in separate divisions.
      • Relational databases are run only on one server, which means that if you want your DBMS to cope with a larger amount of data, you need to invest in costly physical equipment.

      These drawbacks have forced developers to search for alternatives to relational databases. As a result, NoSQL and NewSQL databases have emerged.

      NoSQL databases

      NoSQL databases, also called non-relational or distributed databases, serve as an alternative to relational databases. They can store and process unstructured data (data from social media, photos, MP3 files, etc.), offering developers more flexibility and greater scalability.

      Data in non-relational databases can be changed on the fly without affecting existing data. Additionally, NoSQL databases can be run across several servers, so scaling them is cheaper and easier than scaling SQL databases.

      And since NoSQL databases don’t rely on a single server, they’re more fault-tolerant. This means that if one component fails, the database can continue operating.

      But NoSQL databases are less mature than SQL databases, and the NoSQL community isn’t as well defined. Also, NoSQL databases often sacrifice ACID compliance for availability and flexibility.

      NoSQL databases can be divided into four types:

      • Key-value stores

      This is the simplest type of NoSQL database, which can store only key-value pairs and offers basic functionality for retrieving the value associated with a key. A key-value store is a great option if you want to quickly find information with a key. Amazon DynamoDB and Redis are the brightest examples of key-value stores.

      The simple structure of DynamoDB and Redis makes these databases extremely scalable. With no connection between values and no construction schemes required, the number of values is limited only by computing power.

      That’s why key–value stores are used by hosting providers like ScaleGrid, Compose, and Redis Labs. Often, developers use key–value stores to cache data. These stores are also a good option for storing blog comments, product reviews, user profiles, and settings.

      This type of database is optimized for horizontal scaling, which means you need to add more machines to store more data. This is less costly than scaling relational databases but may lead to high utility costs for cooling and electricity.

      But the simplicity of key-value stores can also be a disadvantage. With a key–value store, it’s hard or even impossible to perform the majority of operations available in other types of databases. While searching by keys is really fast, it can take much longer to search by values.

      In most cases, key-value stores are used in combination with a database of another type. In the Healthfully and KPMG apps we developed, we used the Redis key–value store in combination with the PostgreSQL relational database management system.

      • Document stores

      Document-oriented databases store all information related to a given object in a single BSON, JSON, or XML file. Documents of the same type can be grouped into so-called collections or lists. These databases allow developers not to worry about data types and strong relations.

      A document-oriented database usually has a tree or forest database model. A tree structure means that a root node has one or more leaf nodes. A forest structure consists of several trees. These data structures help document stores perform a fast search. While this makes it difficult to manage complicated systems with numerous connections between elements, it lets developers create document collections by topic or type.

      For instance, if you’re creating a music streaming app, you can use a document-oriented database to create a collection of songs by Rihanna so users can easily and quickly find her tracks.

      To be flexible, document-oriented databases neglect ACID guarantees. MongoDB and Couchbase are great examples of document-oriented databases.

      Thanks to their structure and flexibility, document-oriented databases are commonly used for content management, rapid prototyping, and data analysis.

      • Column store

      A columnar database is optimized for fast retrieval of columns of data. Column-oriented databases store each column as a logical array of values. Databases of this type provide high scalability and can easily be duplicated.

      A column store deals well with both structured and unstructured data, making database exploration as simple as possible. Columnar databases process analytical operations fast but show bad results when handling transactions. Apache Cassandra and Scylla are among the most popular column stores.

      • Graph store

      In a graph store, each entity, which is called a node, is an isolated document with free-form data. Nodes are connected by edges that specify their relationships.

      This approach facilitates data visualization and graph analytics. Usually, graph databases are used to determine the relationships between data points. Most graph databases provide features such as finding a node with the most connections and finding all connected nodes.

      Graph databases are optimized for projects with graph data structures, such as social networks and the semantic web. Neo4J and Datastax Enterprise are the best examples of graph databases.

      NewSQL – combining the best of SQL and NoSQL databases

      Particular attention should be given to NewSQL, a class of relational databases that combines features of both SQL and NoSQL databases.

      NewSQL databases are geared toward solving common problems of SQL databases related to traditional online transaction processing. From NoSQL, NewSQL inherited optimization for online transaction processing, scalability, flexibility, and a serverless architecture. Like relational databases, NewSQL database structures are ACID-compliant and consistent. They have the ability to scale, often on demand, without affecting application logic or violating the transaction model.

      NewSQL was introduced only in 2011, and it still isn’t that popular. It has only partial access to the rich SQL tooling. Flexibility and a serverless architecture combined with high security and availability without requiring a redundant system increase the chances for NewSQL databases to become a next-gen solution for cloud technologies.

      ClustrixDB, CockroachDB, NuoDB, MemSQL, and VoltDB are the most popular NewSQL databases.

      In the next section, we discuss the distinction between online analytical processing (OLAP) and online transaction processing (OLTP), as your choice of database will depend on whether you’re planning to analyze your data.

      OLAP vs OLTP systems

      Your choice of data storage can also depend on the purpose of data processing. There are two common approaches to processing data: online analytical processing and online transaction processing.

      • OLTP requires data from ACID-compliant relational databases. OLTP is responsible for running critical business operations in real time. For example, it is used for online banking and online shopping systems that capture multiple database transactions from multiple users.
      • OLAP systems, in turn, focus on analyzing historical data and require the best analytics databases along with a large data storage system: a data warehouse, data mart, or data lake, depending on the type of data processed.

      End users of OLTP systems are employees that, for instance, need to ensure that multiple customers can easily use company services simultaneously. OLAP systems are necessary for data scientists and data analysts to analyze data and generate insights, reports, and dashboards. Thus, if you’re planning to make use of big data analytics in your project, you should opt for non-relational databases along with a data warehouse or a data lake on top of them.

      It can also happen that you’ll need both OLTP and OLAP systems for your business. Such a combination is also possible, and it proves to be efficient for maximizing the potential of your data.

      As you can see, there are multiple factors to consider when choosing the right database. In the next section, we look at other criteria you’ll need to take into account when analyzing different types of database systems.

      Read also: How to develop an enterprise data warehouse from scratch to foster a data-driven culture

      More things to consider when choosing a database

      There are several aspects you should pay attention to when answering the question What type of database should I use?

      • Data type. SQL databases are perfectly suited for storing and processing structured data, while NoSQL databases are the best solution for working with unstructured or semi-structured data. If you will manage both structured and unstructured data, you can opt for mixing SQL and NoSQL databases.
      • Scalability. As your web product grows, its database should grow as well. Your choice of database may be affected by the type of scaling you prefer, whether horizontal or vertical. Non-relational databases with their key–value stores are optimized for horizontal scaling, while relational databases are optimized for vertical scaling.
      • Security As it stores all user data, a database should be well-protected. ACID-compliant relational databases are more secure than non-relational databases, which trade consistency and security for performance and scalability.
      • Integration. Important note for choosing a DBMS: make sure that your database management system can be integrated with other tools and services within your project. In most cases, poor integration with other solutions can stall development. For instance, ArangoDB has excellent performance, but libraries for this DBMS are young and lack support. Using ArangoDB in combination with other tools may be risky, so the community suggests avoiding ArangoDB for complex projects.
      • Analytics capabilities. Your choice of database and data management system also depends on the type of analytics you’ll want to perform. For instance, if you need to store large amounts of structured data for further analysis, you should also set up a data warehouse. If you need to store and analyze big data or large amounts of unstructured data, on the other hand, you should choose a data lake. Learn how we helped a 3PL company aggregate and analyze big data from multiple sources with the help of a data lake.

      Read also: BI and advanced analytics solutions for supply chain data analysis

      List of popular database management systems (DBMSs)

      Want to know the most popular databases for 2023? Let’s check out the following list of top databases:


      OracleDB, an RDBMS developed in 1977, remains the most popular database and the most trusted solution on our list of database applications. It’s ranked first in the DB-Engines Ranking. Let’s look closely at the reasons for OracleDB’s popularity:

      • It’s backed by Oracle and, hence, is reliable. Developers point out that OracleDB rarely goes down and receives regular updates.
      • It scales well and is considered the best database for large datasets. Oracle is currently bringing all its products and services to the cloud, resulting in more flexibility.
      • It’s secure, scrupulously following modern security standards (including PCI compliance) and offering good encryption of sensitive data.
      • It manages memory very efficiently and easily handles complex operations. Also, it effectively manages and organizes a variety of third-party tools.
      • It outperforms other solutions in terms of speed of data access across the network.

      But OracleDB has downsides as well:

      • With its the most popular DBMS, OracleDB is also one of the most expensive. A Processor License for the Standard Edition will cost you $17,500 per unit.
      • Oracle has complicated documentation and lacks good guides. Even though customer support is helpful, some developers complain about long response times.
      • These factors make OracleDB the type of database that would be best to store large amounts of data. Small and midsized businesses should search for more cost-effective alternatives.


      MySQL is also on the list of popular databases and is one of the most used database software solutions. A relational database management system, MySQL was created in 1995 and is managed by Oracle. This open-source database system has a huge user base and great support, and it works well with most libraries and frameworks. It’s free, but it offers additional functionality for a fixed price.

      Developers can install and use MySQL without spending long hours setting it up. Most tasks can be done in the command line. This is a well-structured database that receives regular updates.

      MySQL works perfectly with structured data at the basic level. But if you’re considering scaling your product in the future, you may need additional support, which costs a pretty penny. Also, it takes a lot of time to create incremental backups or change the data architecture in MySQL, while its rivals can do this automatically.

      Uber, Facebook, Tesla, YouTube, Netflix, Spotify, Airbnb, and many other companies use MySQL for their services. We also use this DBMS for our projects.


      This is an object-relational database, which means that it’s similar to relational databases, only all data is represented in the form of objects instead of columns and rows.

      PostgreSQL is the best data management system for large software solutions. It’s scalable and designed to handle terabytes of data, and a hierarchy of roles to maintain user permissions means advanced security.

      Unlike MySQL, PostgreSQL is completely free. Its open-source nature means that all documentation and support are provided by enthusiastic volunteers. It also means that in case you have problems with PostgreSQL, you’ll need to search for an expert who can solve them.

      We migrated World Cleanup, an app for managing the World Cleanup Day event, from CouchDB to PostgreSQL. Migrating to PostgreSQL let us not only perform in and out operations simultaneously but also easily handle high loads.


      MongoDB is the most common database we use in our projects, and it’s the best database for web apps. MongoDB is a NoSQL database that stores all data in BSON (Binary JSON) documents. Thanks to this, data can easily be transferred between web applications and servers in a human-readable format.

      MongoDB has onboard replication, providing high scalability and availability. Auto-sharding means you can easily distribute data to servers connected with your app. In general, MongoDB is the best web database for dealing with massive unstruсtured data sets. It can underpin most big data systems, not only as a real-time operational data store but also in offline capacities.

      But there are several pitfalls of this database platform. It stores key names for each value pair, increasing memory use. Also, there are no foreign key constraints to enforce consistency, and you can perform nesting for no more than 100 levels.

      In combination with Redis, we used MongoDB in Boothapp, a social e-commerce platform for the Middle Eastern market.


      Redis is an open-source key–value store that’s often used as a caching layer to work with another data storage solution. The main reason why developers opt for Redis is its speed, which far outstrips other database management systems. It’s also easy to set up, configure, and use.

      But Redis lacks built-in encryption and stores only five data types: lists, sets, sorted sets, hashes, and strings. The main purpose of Redis is to store data sets without a complex structure. That’s why this tool is usually paired with another type of database system and is sometimes used for microservices. Since Redis is a great solution for caching, we use it for this purpose in most of our projects, including in the KPMG, Half Cost Hotels, Mikitsune, and Healthfully apps.


      Elasticsearch is an open-source document-based database that stores and indexes any kind of data – text, numerical, or geospatial – in JSON format. By doing so, it enables fast search and data retrieval. Elasticsearch is built on Lucene, an open-source Java software library that it uses to store and search for data.

      One of the major reasons why Elasticsearch is so popular is its scalability. It easily scales horizontally, allowing for the extension of resources.

      Starting from Elasticsearch version 6.7, users can manage the data life cycle. Data can be referred to as hot, warm, or cold depending on the number of requests for it and can be stored in hot, warm, and cold data nodes respectively. This functionality allows you to retrieve the most relevant (or the hottest) data quicker, as hot nodes use solid state drives (SSDs), a newer and faster type of storage device. Warm and cold nodes need only traditional hard disk drives (HDDs), which are slower.

      Netflix, Stack Overflow, LinkedIn, and Medium rely on Elasticsearch.


      This is an open-source column-oriented DBMS that can generate analytical data reports in real time. It was released to open-source only in 2016 and started to get popular fast. Advantages of ClickHouse include:

      • High performance
      • Fault tolerance
      • Scalability
      • Possibility to store lots of data thanks to data compression

      Also, ClickHouse supports an extended SQL-like language, which is definitely a plus for developers.

      ClickHouse is already in use in companies like Uber, eBay, Spotify, and Deutsche Bank.

      Find out more about Yalantis’s expertise, working with databases In our recent projects: Healthfully, Lifeworks, ERX platform.

      Mixing and matching databases 

      You can use several databases in one project. But combining two databases isn’t always a good idea. Developers should make this decision only after carefully analyzing a project’s needs and defining the product’s technology stack.

      Redis is often used in combination with other databases. We used Redis in combination with PostgreSQL for Healthfully, a medical platform to connect patients and medical professionals. We chose Redis for cache and token storage since it works faster than most modern databases. For the same reason, we used Redis together with PostgreSQL when developing an app for KPMG. We commonly use this pair in our projects, since we can quickly and easily make references from Redis to PostgreSQL.

      Using MongoDB and PostgreSQL is a bad idea, since these databases are equal in terms of resource use and data storage. For instance, say you have a social network like Instagram and need to store information about posts, likes, followers, and user profiles. You store data about likes and posts in MongoDB, while user profiles and followers are stored in PostgreSQL. In this case, you would first need to retrieve data about profiles from PostgreSQL, then get data about posts from MongoDB, which is a time-consuming and inefficient solution.


      As you can see, your choice of a database for your project depends on many factors, including the types of data you’re going to collect and process, integrations with other tools, and the scaling approach you follow. It’s not just a question of SQL or NoSQL, as many think.

      And even though proper data management may not be the first thing you consider when optimizing the user experience, it definitely should be. We can help you find the best possible database solution for your web or mobile app. Drop us a line if you want us to help you in selecting the right database for your needs.

      Discover what technologies we’re good at

      We can help you create a scalable app that can easily withstand high loads

      See our expertise

      Forward-thinking industrial IoT solution providers have made great strides in connecting their products and equipment to the Industrial Internet of Things (IIoT). These providers produce sensors and industrial gateway IoT hardware to enable IIoT monitoring. This hardware allows users to combine smart things into IoT networks. Offering industrial IoT solutions and services, some of these providers: 

      • have built industrial IoT systems that collect data from sensors, process it, build trends, send alerts, and control sensors 
      • offer their customers platforms for centralized access to IoT networks and their troubleshooting 
      • add full-fledged modules that help to process business data collected from sensors for improved decision-making  

      If you fit into one of the categories above, you’ve come to the right place. In this article, we view challenges, aims, and specifics of industrial IoT software development to cover your and your customers’ needs. These insights on industrial IoT solution development should help you ensure quality industrial IoT gateway design and build industrial IoT solutions that meet customers’ needs and expectations while minimizing technical and other associated risks. In this article, we also discuss IoT in manufacturing, agriculture, and other domains.

      Before viewing the specifics of industrial IoT design, we need to differentiate IIoT and consumer IoT.

      Read also: How to ensure remote management of large IoT networks

      Differentiating industrial IoT and consumer IoT

      Since this article is solely devoted to IIoT, let’s first figure out what IIoT is in comparison to consumer IoT. A consumer IoT network commonly encompasses several consumer devices. All of them have a limited lifetime of several years. On the contrary, an IIoT network is required to connect hundreds if not thousands of devices to maintain operations of costly industrial equipment over decades.

      Below, we list the key differences between consumer IoT and IIoT. Take into account the peculiar properties of IIoT you see in the table while analyzing business requirements and developing technical solutions.

      In the next section, we cover the main IIoT domains and their peculiarities.

      Examples of problems IIoT helps to solve for different industries 

      While the word “industrial” may bring to mind warehouses, shipyards, and factory floors, IIoT technologies hold lots of promise for a diverse range of industries: agriculture, pharmaceuticals, and more. Let’s list some of the industries where industrial IoT automation is most prominent:  

      Agriculture. Using IIoT software helps farmers collect data on soil, nutrients, and moisture, using visual and thermal imaging to detect potential problems. One peculiarity of this industry is the large geographical area of the fields in which data is collected.

      Manufacturing. IoT in manufacturing enables condition-based maintenance and data-driven process improvements. Using low-speed connections, automation, and digitized production, it’s possible to improve process and equipment stability, increase durability, and mitigate risks related to equipment malfunctions and violations of environmental norms.

      Pharmaceutical. In the pharmaceutical industry, IoT devices can ensure condition and environmental monitoring throughout medicines manufacturing, storage, and transportation. This includes monitoring and maintaining air quality and ambient temperature requirements. 

      Mining, oil, and gas. IoT manufacturing solutions for the mining, oil, and gas industry can help control pumps and synchronize data from multiple sites. Usually, such solutions are used in a large production complex in which the team measures environmental indicators, performance indicators, and equipment depreciation.

      Logistics. Using IoT, logistics providers obtain real-time visibility on cargo movement and item-wise monitoring so all items arrive where and when needed. IoT-powered warehouse management ensures real-time visibility regarding inventory levels, which helps to avoid costly out-of-stock events. IoT devices can also track the condition of an item and notify warehouse staff when temperature or humidity thresholds will be exceeded.

      Each domain that is successfully adopting IoT technology is crawling with enterprises and managed service providers (MSPs). Many of them are considering developing their own IoT implementation initiatives, and here’s why.

      Two types of target audiences for an IIoT vendor and how to satisfy their needs

      The IIoT industry is advancing and expanding faster than many businesses realize. The pace of growth is revealed by research from MarketsandMarkets:

      Enterprises and MSPs we talk about further in this article are essential contributors to this growth.

      Enterprises with large IoT networks 

      IDC predicts a big jump in spending on IoT projects among enterprises in the coming years. In its worldwide IoT spending guide, IDC predicts global IoT spending will achieve a CAGR of 11.3 percent over the 2020–2024 forecast period. Such high demand provides great opportunities for IIoT vendors wanting to grow their business.

      What service can you offer enterprises?

      As enterprises prefer to adopt turn-key solutions, you can offer them an IoT management software ecosystem that consists of three layers:

      1. IoT hardware. This includes all physical components: smart devices, microcontrollers and microprocessors, physical casings, user interface components. Ensure a smart industrial design combined with an enjoyable user experience and supporting firmware capabilities to help target users deploy and perform basic analysis on the state of hardware devices.
      2. IoT software. This layer is used by end users, and thereby its development requires UI/UX design, mobile and web application development, and database creation. Also, implementing such a layer requires IoT product cloud development, as the cloud ensures access for all user roles.
      3. IoT connectivity development. This layer ensures connectivity between the hardware and software layers. The purpose of the IoT connectivity development layer is to maintain smooth and real-time data streaming between smart devices and the IoT software.

      Keep in mind that each enterprise has strict requirements for functionality, reliability, transparency, auditing, and reporting. It’s essential to discuss such requirements during industrial IoT consulting. By the way, we provide such a service. If you want to learn what IoT services we offer, see our IoT-related expertise.  

      Managed industrial IoT services providers

      According to Fortune Business Insights, the size of the global managed services market is projected to reach $557.1 billion by 2028 while demonstrating a dramatic CAGR of 12.6 percent between 2021 and 2028. According to Research and Markets, managed services will account for up to 71 percent of all enterprise deployments by 2027.

      MSPs help enterprises adopt edge-to-edge and commercial-ready solutions on a large scale. At the same time, they offer the ability to scale on demand. In addition, MSPs enable enterprises to accomplish higher levels of digital transformation and improve their reach across external and internal supply chains. 

      What service can you offer MSPs?

      MSPs are interested in building or improving existing platforms for IoT hardware monitoring used for data collection, processing, visualization, and device management. When creating such a platform for an MSP, take the following into account: 

      • If the MSP has several customers, they have numerous isolated IoT networks to monitor and manage via a single dashboard.
      • Each end customer should only have access to their own network.
      • The MSP should be able to separate data and reports for each of their customers (e.g. charge customers for a specific number of devices under service).
      • The platform should meet requirements regarding IoT network monitoring, access, and management.
      • The platform should provide a flexible permission model and access management for a multi-customer base.
      • The platform should allow for UI customization (enabling an MSP Industrial Internet of Things company to add branding elements for a customer-facing portal).      

      To offer quality and customer-oriented services, you need to consider what customers expect from your services and their roadblocks on the path to adopting IIoT technology. 

      What problems do your potential customers face when adopting or using IIoT technology?

      Challenge №1: Making use of huge amounts of data

      It’s evident that when adopting IIoT technology, organizations start gathering large amounts of data. However, the matter of making use of this data to accomplish business goals is often left unresolved. Knowing how to use particular data to calculate and track particular KPIs is significant when adopting IIoT tools. When using these tools, lots of organizations just watch the amount of data grow while having no clue how to interpret it.

      Solution: Implement IIoT network data analytics for improved decision-making

      Data collected by organizations can be roughly divided into: 

      • business data (collected by sensors on things like temperature, level of dust in the air, and speed)
      • data on the hardware network’s work (noise level, system efficiency, etc.)

      All this data should be sent to a data processing center or be processed in real time for further decision-making. 

      It’s important to note that the way data is handled will depend heavily on the particular domain and use case. Here, we describe some approaches for dealing with IIoT data:

      Data analysis and processing. Because IoT data comes in large volumes, performing real-time analytics requires the ability to ingest and enrich data with sub-second latency so that data is ready to be consumed in real time. There are many open-source analytics frameworks or industrial IoT platforms that can be used to provide IoT data processing and analytics for your IoT solutions. Analytics can be performed in real time as the data is received or through batch processing of historical data. 

      Read also: IoT data analytics and IIoT data management

      Data storage. Data can be stored on-premises, somewhere in the cloud, or using a hybrid of these two approaches. Important considerations in deciding on the right data storage strategy are the data volume, network connectivity, and power availability. Another thing to consider is that different data is destined for different purposes. Data intended for archival purposes and data intended for real-time analytics can be stored using different approaches. Data access needs to be fast and support querying for discrete real-time data analytics. There’s also an emerging trend of edge computing to perform data pre-processing on-premises before pushing data to the cloud.

      Data visualization. Visualizing data allows you to display big data in a meaningful way to better understand it. Visualizing data from multiple sources on a dashboard helps you make real-time decisions. Additionally, combining new IoT data transmitted from sensors with existing data can bring to light new business opportunities. You may also be interested in reading about data visualization in logistics.

      Also, AI-powered and big data tools have achieved a good track record for ensuring data quality analysis, data visualization, and reporting. Depending on the use case, our Yalantis experts will help you choose the right tools for each of these approaches.

      Challenge №2: Interoperability between systems in use

      The most forward-thinking organizations have stopped leaning on legacy data systems and switched to modern software solutions like ERPs and MESs. Enterprises refuse to spend a fortune and a vast amount of resources on new tools that require closing gaps between systems. Therefore, such organizations are selective in choosing new software to make sure it’s flexible enough in terms of integration with existing systems.

      Solution: Ensuring seamless industrial IoT integration into data systems by means of an IIoT integration service

      To be competitive, you should offer your customers the easiest path towards innovation and allow organizations to make just a few adjustments in order to add intelligence, IoT devices, and automation on top of existing infrastructure.

      Enabling communication between sensors and the internet requires middleware that provides interoperability all over a network by transforming data from one protocol to another. For instance, open platform communications (OPC) has proven to be effective communications middleware that ensures data sharing over networks, tackling interoperability challenges.

      Challenge №3: Security risks

      Along with its indisputable benefits, IoT has introduced some new security challenges. Growing security concerns, including software vulnerabilities and cyberattacks, can make lots of customers refrain from using IoT devices. Security incidents are especially harmful for companies operating in the energy, gas, oil, healthcare, finance, assembly, supply chain, and retail sectors.

      The cyber attack on Oldsmar is a prominent example of the need for ensuring a high level of IIoT cybersecurity. In 2021, cybercriminals managed to access a computer monitoring chemicals used to treat drinking water for the city of Oldsmar. Attackers changed the level of sodium hydroxide from the normal 100 parts per million (ppm) to 11,100 ppm. Fortunately, the issue was promptly recognized, and normal operating parameters were brought back before any damage could be done.

      The incident provoked reasonable questions. How did the industrial facility become accessible from the web? Why were remote access capabilities installed with no appropriate security policies and without reliable authentication? How could cybercriminals set process parameters to alarming levels with no additional authorizations and controls? Unfortunately, such incidents are common, and you need to take measures to protect your IIoT infrastructure. 

      Solution: Stepping up comprehensive security measures 

      IIoT infrastructure must be protected by a comprehensive set of security solutions that do not disrupt operations, reliability, and profitability. A simple and practical solution that can be easily and widely deployed is more effective than a superior solution that cannot be widely implemented. Security solutions should include the following features:

      Secure boot. The cryptographic code signing techniques used in secure boot technology ensure that a device only executes code that has been created by a trusted entity (e.g. the hardware manufacturer). This technology prevents hackers from replacing firmware with a malicious set of instructions. If your IIoT chipsets are not equipped with secure boot functionality, it’s important to ensure that IIoT devices can only communicate with authorized services to avoid the risk of firmware being replaced with malicious instruction sets.

      Reciprocal authentication. Cybercriminals can use a lack of reciprocal authentication to insert rogue or shadow IoT devices into the network and execute active and passive attacks. Based on the specifics of IIoT security scenarios and risks, device identity management and secure authentication is important. A security solution might include identity provisioning and management, network-wide key/credential management with proper automated rotation, and other mechanics. One more option is to use three-factor authentication frameworks that provide mutual entity authentication of the gateway with the remote user (subscriber) and the IoT node (publisher), along with session key generation. In three-way authentication, the central authority authenticates the two parties and helps them to authenticate each other.

      Secure communication (end-to-end encryption). There are several schemes for exchanging, confirming, and periodically rotating encryption keys. These schemes were developed and tailored to IoT networks for ensuring reliable encryption during data transmission. 

      Security monitoring and analysis. It’s important to ensure that end devices are protected from possible hacking and data manipulation, which can lead to misreporting of events. After collecting data about the overall state of the system through security monitoring, this data is analyzed to identify possible security breaches or potential system threats. 

      Cybersecurity certifications.  IEC 62443 is a set of internationally recognized industrial IoT standards that establish the operation and product requirements for the secure development of IoT in Industrial Automation and Control Systems. 

      For more on the topic, read about our cybersecurity expertise.

      So far, we have given a brief analysis of the business context and target audience for IIoT devices, the needs and challenges of the target audience, and some tips on how to overcome them. Next, we share solutions that will help you create IIoT software fast and securely.

      Before diving into the niceties of IIoT software development, we want to mention that we can offer team augmentation services for your IoT project. Check out our case study on one such project.  

      What to consider during IIoT platform development

      Based on our extensive IoT software expertise, we can help you create sophisticated industrial IoT solutions and services to optimize maintenance, enhance secure data collection and transfer, and improve operations with a meaningful solution on top of your existing IoT infrastructure. While building IIoT software, we take into account the following:

      Enabling flexible remote setup. Building a remotely controllable cloud platform is a great solution for simplified setup and maintenance of large IoT networks. Such a platform will enable you to simultaneously set up, synchronize, and replace hardware. To provide remote management, use dedicated IoT protocols like LWM2M or MQTT. Using such protocols will also let you seamlessly integrate your IoT solution with your own and third-party systems if needed. We created such a platform for remote IoT device management. This is a SaaS solution that improves setup and maintenance of large IoT networks.  

      Ensuring business continuity. In the context of developing industrial IoT solutions, business downtime is expensive and unacceptable. That’s why sufficient flexibility must be built into both the equipment and the remote control system. For example, easy device replacement, enhanced monitoring, and alerting features must be provided with the number of devices in mind. You can read more on this topic in one of our recent articles. You may also find more information and best practices in our article about remote management of large device networks.    

      Enabling bulk operations. As large IoT networks are common for IIoT, you’ll need to develop logical entities that will combine different IoT devices for provision of bulk operations (device onboarding, firmware updates, etc.).

      Providing multitenancy. As MSPs manage multiple customer deployments and provide customer teams with access to the same remote management monitoring system, multitenancy is a critical security and architectural requirement. Making sure that assets and data are properly isolated and visibility is strictly managed is a must. 

      There is no benchmark for IoT development and designing an industrial IoT architecture, as the industry is young and developing. Therefore, for industrial IoT companies, it’s important to find a software provider with relevant expertise. Yalantis is one of the IoT platform companies with experience building solutions to optimize equipment maintenance operations and enable visibility in asset use. Get in touch if you want to create or improve your IIoT software. 

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      What are industrial IoT monitoring use cases?

      IIoT monitoring cases vary and can deliver an array of benefits depending on the company and what IIoT monitoring is used for. The most common use cases include predictive maintenance to prevent costly equipment’s breakdowns and repairs, reducing energy usage by optimizing the use of equipment and/or production, and quality assurance of resources and products.

      What are the types of industrial IoT platforms?

      The ability to manage and supervise industrial IoT devices and data they provide is the main benefit of using an IIoT platform. However, such platforms vary by capabilities to be useful for different use cases. Actually, it would be an exaggeration to say that there are specific types of IIoT platforms. Rather, IIoT suppliers are expanding their platform capabilities to satisfy growing clients’ expectations and particular business needs. Modern IIoT platforms provide users with a different mix of capabilities including IIoT endpoint management and connectivity, data ingestion and processing, and data visualization and analysis.

      Why choose Yalantis for IIoT software development?

      We are experienced in IoT platform development, cloud IoT migration, and enabling IoT analytics. Specifically, we build IIoT network management platforms for IoT suppliers who target managed service providers and large manufacturers. Check out our IoT expertise, case studies, and clients’ reviews to explore our polished IoT software development processes, our IoT products’ capabilities, and the project results.