Big data services
Forecast market changes or consumer demands with predictive analytics and efficiently adjust your business processes
Generate insights from unstructured data to drive innovative decision-making and increase competitiveness
Identify issues and bottlenecks right away by setting up a real-time data collection and analysis process
Ensure unhindered data access and secure data storage by handling large datasets at ease in a single source of truth
Streamline data exchange within the entire organization by building data infrastructure that collects data from all critical internal and external sources
Generate extensive reports and dashboards to visualize company achievements and analyze the performance of each department
Big data solution services Yalantis provides
Generate insights from all relevant data sources and stay ahead of the industry
Build a single source of truth within your organization to get quick access to critical datasets and benefit from big data analytics.
Use cases Yalantis big data service providers focus on
Financial market analysis
Prediction and prevention of fraudulent activity and malicious attacks
Investment risk assessment
Personalized banking services
Collection of customer behavior data
Real-time data collection and analysis from multiple IoT devices
Monitoring and control of remote systems and devices
Cost-efficient and optimized IoT network management
Device quality control
Energy and utilities
Energy consumption monitoring
Efficient renewable energy management
Prediction of electricity network outages
Assessment of electric vehicle (EV) adoption benefits
Optimal energy transmission and distribution
Benefits of partnering with our big data company
Vast data engineering experience
Get expert consultancy on a wide array of data management issues to ensure seamless integration of big data solutions. Enhance business decision-making with a big data analytics flow that perfectly aligns with your business needs.
All-around big data consulting
Begin your big data journey safely with a deep-dive research stage to ensure implementing a big data solution is suitable for your business. Develop a clear and iterative implementation roadmap to forecast any risks and avoid unexpected bottlenecks.
In-depth exploration of data sources
Ensure you use all possible internal and external data sources relevant to your industry. Collect data from social media, news, consumer forums, or any other sources to offer services that are in demand and bring real value.
Cost-effective data infrastructure
Select modern and reliable data management tools and technologies at a reasonable price. Ensure integration of as many ready-made solutions as possible and develop from scratch only those components that have validated business-specific potential.
Focus on data security
Ensure secure integration of a big data solution and protect your sensitive business data from malicious attacks. Implement a custom set of security controls to monitor big data system performance and make timely security patch updates.
Enhanced business continuity
Streamline business operations with an end-to-end big data solution. Enable uninterrupted data exchange within an entire organization and generate insights from diverse data types in real time to improve your service delivery and increase customer satisfaction.
Yalantis: A company with a proven portfolio
Supply chain big data analytics system
Explore how Yalantis implemented a technologically challenging and complex big data analytics solution for a US-based 3PL company.
Data lake for a manufacturing company
Learn how Yalantis helped a manufacturing company set up a data lake solution to ensure supply chain visibility and optimize use of manufacturing data.
Streamline your business processes with a custom big data system
Develop a big data analytics solution that helps you solve critical operational issues by making sense of large amounts of data.
Video reviews of our clients
What triggered us was their remote collaboration practices as well as their experience in the IoT industry. Their strong technical experience helped us scale our platform and deliver great performance to our customers.
Yalantis has been a great fit for us because of their experience, responsiveness, value, and time to market. From the very start, they’ve been able to staff an effective development team in no time and perform as expected.
Working with Yalantis, you get their breadth of experience building hundreds of projects. Their expertise and knowledge were second to none. And that makes the difference between a good product and a great product.
Insights into our big data services
Central repository for your data: data mart, data warehouse, or data lake?
Learn the differences between a data warehouse, a data mart, and a data lake and pick the right data repository to cover your industry-specific needs.
Guide to real-time data processing
Find out key stages of handling real-time big data efficiently, learn what tools and technologies to use to streamline this process.
How to develop an enterprise data warehouse from scratch to foster a data-driven culture
Get a set of clear steps to develop a domain-driven EDW design; learn about integral elements of an EDW and key stages of its development.
Assess your business readiness for a big data solution
Count on Yalantis experts to prepare your business data infrastructure for seamless implementation of big data analytics.
What technologies and tools does your big data company work with?
For every unique big data project, Yalantis data science specialists opt for a set of technologies and tools depending on the technical and business needs. For instance, for batch and stream processing of big data, we use Apache Spark, which is an open-source platform that allows for fast in-memory data processing.
For real-time stream processing, we use Apache Storm and Apache Samza. These frameworks are easy to use, support more programming languages than Apache Spark, and are specifically designed for real-time stream processing.
To set up a proper extract, transform, load (ETL) process, organize well-suited data storage, and ensure insightful data visualization, our big data services team also uses various Amazon Web Services (AWS). For instance, Amazon Data Exchange allows for accurate data collection from third-party services, Amazon S3 serves as scalable big data storage, and Amazon QuickSight is useful for ML-powered data visualization.
How long does it take your big data solutions company to implement a custom big data product?
The time it takes to implement a big data solution depends on multiple factors, including the following:
Project scope. Depending on the volume and types of data we need to work with, implementing a big data project can take from three to nine months.
Project deadlines. If you need to develop your project within a strict timeframe (for example, in order to present at an industry-related event), we can compose a larger team than usual and ensure faster development. However, such an approach can be costly, as it requires us to apply extra effort.
Business urgency. If you have urgent business issues to solve with a big data solution, our big data solution services team can offer an iterative development approach that allows you to test the solution and gradually solve your issues.
High quality and accuracy are critical for our big data services company, and that’s why a project may sometimes take longer than expected — but the result will be worth the wait.
What types of data sources and data types do you work with as a big data service provider?
We have experience working with all types of data: structured, unstructured, and semi-structured. As for data sources, we’ve had projects that required collecting data from IoT devices, third-party services, social media platforms, news websites, customer surveys, and reports scattered across diverse internal software such as CRM, ERP, and HRM systems.
You can provide the Yalantis team with an approximate list of sources to extract data from. Big data service providers can research additional sources that can be beneficial for your business and industry. Then, we will work on designing an architecture that ensures your data is aggregated accurately, either in real time for immediate analysis or in bulk for storage and further use.
Does your big data company provide advanced analytics services on top of big data services?
Sure. For a big data solution to bring maximum potential to your business, Yalantis’ big data services team can set up an advanced analytics process. To do this, we first research advanced analytics tools and solutions that suit your business needs.
Next, we integrate these solutions into your architecture design. Such services can include Amazon Athena for advanced analysis of data stored in a data lake or Amazon SageMaker, which implements machine learning to enable predictive analytics.
After seamless integration of advanced analytics services, our data scientists test how these solutions perform and see whether they perfectly fit into your workflow and help you by enhancing business decision-making and improving business operations.
Principles that big data service providers follow to develop a real-time big data analytics solution
As a mature big data service provider, we can offer you a list of principles that are important for developing a successful real-time big data analytics system. If you’re considering cooperating with big data companies, you should know these principles to form a general understanding of the development process.
Choosing tools and technologies for stream processing
Setting up stream processing for big data is essential to enable real-time big data analytics, as incoming data should quickly get into services that can analyze it and generate business insights.
Such platforms as Apache Storm and Apache Spark along with Amazon Kinesis big data services are used for establishing an efficient big data processing flow. In particular, Amazon Kinesis Data Firehouse is an ETL tool that captures, processes, transforms, and transfers large datasets directly to a data analytics service.
Designing a real-time big data analytics architecture
Our big data services company defines flexibility and scalability as the most critical characteristics of an architecture suitable for real-time big data analytics. With these non-functional requirements in mind, your big data analytics system can handle high loads and adapt to shifting industry demands.
An event-driven architecture is the most frequently used for real-time big data analytics systems. Such an architecture allows for ensuring high system performance and can process a large number of requests.
Establishing an advanced analytics process
Integrating services that enable advanced analytics is an important part of big data analytics solution development. To derive meaningful insights from data, big data service providers incorporate advanced analytics and AI/ML capabilities into their solutions.
These technologies enable the identification of patterns, anomalies, and trends in real-time data streams. By applying machine learning algorithms, organizations can automate decision-making and gain a deeper understanding of their corporate data scattered across diverse data sources.
User-centered interface based on a comprehensive UX survey
Yalantis’ big data services also include a holistic approach to the UI/UX design of your real-time big data analytics solution. It’s important that not only technical specialists but also non-technical can use the system.
That’s why we perform a UX survey among future end users of your big data analytics solution to define the flow that would be the most convenient for them and help them easily navigate the system and effectively generate critical business insights.
System monitoring and timely alerts
As a reliable big data solutions company, we also ensure that a real-time big data analytics system has a notification center to proactively alert admins of any roadblocks that need to be addressed. Round-the-clock and comprehensive solution monitoring and maintenance are features that differentiate Yalantis from other big data companies.
What’s big data as a service (BDaaS) and how can your business benefit from it?
Big data as a service (BDaaS) refers to a wide range of services including end-to-end big data services such as big data collection, establishment of data warehouses, and data lakes for scalable storage of large amounts of structured and unstructured data.
BDaaS is a cloud-based service model that provides organizations with access to the tools, complete data infrastructure, and expertise needed to manage and analyze big data. Such a service model allows businesses to harness the power of big data without the complexities and upfront costs associated with building and maintaining their own on-premises data infrastructure. Here are some key aspects of BDaaS and how your business can benefit from it:
Scalability to make on-demand changes whenever needed
If you choose to partner with a BDaaS provider, you can benefit from extremely scalable solutions, allowing your business to easily accommodate growing data volumes or a growing number of users.
As needed, you can scale your solution up or down based on your needs without much hassle or any hardware setup on your end. Thus, cloud-based big data services are beneficial, as you can save time and money while maintaining a flexible software solution.
Cost-efficiency with a subscription-based approach
In most cases, you don’t need to make substantial upfront investments for big data as a service in terms of hardware, software, and IT staff. You pay only for what you use on a subscription or pay-as-you-go basis. And in case you no longer need a particular feature or support, it’s much easier to stop paying for it if you have a partnership with a BDaaS company.
Regardless of your business’s size, BDaaS proves extremely cost-effective. Small companies may not have enough capacity to develop a big data solution from scratch, and large organizations or enterprises can reduce operational expenses and save money for meeting urgent business needs.
Reduced maintenance overhead
Managing on-premises big data infrastructure can be resource-intensive, and that’s one reason why organizations look for a trusted big data solutions company. BDaaS takes the load off your team when it comes to diverse IT maintenance tasks like hardware and software updates, allowing your IT team to focus on more strategic initiatives.
Speed and agility to accelerate time to market
Big data service providers offer a wide array of preconfigured data environments and tools, enabling rapid deployment of big data solutions. Therefore, you can count on a shorter time to market for your big data products and derive tangible value from them much faster than you would if you had to set up an on-premises big data management flow.
Such agility also allows your business to respond quickly to changing data requirements and market conditions, gaining a competitive edge.
Automated data management that saves time
BDaaS solutions include data storage, processing, and management capabilities. You can rely on automated big data services for real-time data ingestion, prompt transformation, accurate cleansing, and secure storage, making it easier to use your data effectively. Maintaining full-blown data infrastructure on your own is tedious, and having a reliable big data service partner constantly available is a business time-saver.
Analytics and insights to encourage business process visibility
When you choose a big data as a service platform, you can also get access to powerful analytics tools and machine learning capabilities. Compared to custom software development, with BDaaS, you don’t need to engage in a prolonged research phase to choose the right advanced analytics services for your business.
Such tools come built into your BDaaS platform and can help your business extract valuable insights from your data, leading to data-driven decision-making and improved business outcomes. However, partnering with a reliable software partner can also be necessary to fine-tune your data and advanced analytics tools for a proper analytics process.
Security and compliance to meet big data service standards and user expectations
Reputable and trusted BDaaS providers invest heavily in strong security and compliance measures to protect your data. Since BDaaS is cloud-based, big data services providers ensure robust security controls, solid data encryption, as well as compliance certifications, which can help you meet industry-specific regulatory requirements.
Another important aspect of establishing strong security measures is your end users’ security expectations of big data software, as you need to assure end users that their data is well-protected. In general, it’s critical to ensure users that your environment is safe and secure.
Global reach allowing for efficient big data service troubleshooting
BDaaS providers often have data and service centers in multiple geographic regions, allowing your business to analyze and store data closer to your end users or easily comply with local data requirements.
With big data as a service, your business can be more flexible, as even if you expand your headquarters to different countries, you’ll still be able to have the same access to your big data management flow and ensure your services won’t be disrupted. With a mature and experienced BDaaS provider, you aren’t limited either in your location or in your access to IT staff or resources.
Collaboration and accessibility to simplify the use of big data services
BDaaS solutions can foster cross-platform collaboration among teams and stakeholders, as data and analytics tools are easily accessible via a web platform.
This way you can enable productive remote work and collaboration on a global scale. It’s especially beneficial for large enterprises, as they can ensure all departments work synergistically towards a common goal and overall company success.
Customization to maintain digital brand identity
Cooperating with a big data as a service provider doesn’t necessarily mean that you won’t have any customization options and will only have prebuilt functionality. Often, BDaaS providers offer a wide array of services and configurations, allowing you to cater your big data management platform to your specific business needs.
You can choose the services and expertise that align with your data and analytics requirements, and with time, you can modify the system as you like by adding more resources.