Data engineering services
Reimagine your daily data management practices and open new ways of storing, processing, and accessing data
Update legacy technology, architectural patterns, and systems to efficiently handle increasing data volumes
Keep up with market demands and prepare your data infrastructure for real-time business data analysis
Establish an interconnected corporate data network to leverage all relevant data sources
Reduce the number of siloed data storage systems to increase data accessibility across departments
Improve customer service and employee management by keeping track of comprehensive customer and employee data
Data engineering services Yalantis provides
Enhance your data infrastructure capabilities
Collect, store, process, and organize your data in an efficient way that works for your business.
Industry-specific use cases Yalantis specializes in
Financial data aggregation from internal and external sources
Real-time transaction and payment data processing
Collection of customer behavior data
Regular compliance reporting
Data collection from software systems and medical IoT devices
Integration of multiple electronic health record (EHR) systems
Remote patient monitoring with real-time data processing
Data architecture update to improve system performance
Integration with additional data sources
Implementation of AI or ML components
Updating or optimization of data security controls
Benefits of partnering with data engineering agency
Complete data infrastructure setup
Rely on our experienced team to build and deploy full-blown data infrastructure. Get all-around support in case any issues occur and optimize processes that don’t align with your business needs.
Comprehensive data architecture assessment
Benefit from a thorough evaluation of your current data architecture to spot any issues such as data duplication that slow down system performance and prevent you from meeting your goals.
Scalable data engineering team
Scale up or down your data team depending on the project needs. Take an active part in the hiring and onboarding process to compose a team that shares your business mindset.
Iterative roadmap development
Improve your data management practices and data infrastructure in a gradual and non-disruptive way to ensure your company functions as usual.
Early-on risk analysis
Consider all ramifications of your data management decisions and assess how changes in your data infrastructure may affect your organization.
Resilient data security controls
Ensure compliance with industry standards, laws, and regulations and set up a secure data environment that stores relevant, high-quality data with a role-based access policy.
Yalantis: A data engineering company with a proven portfolio
Data re-engineering to transition to a microservices architecture
Learn how we ensured seamless data transfer when shifting to a microservices architecture and replicated a database across multiple data centers to ensure proper data backups.
Telehealth software enriched with IoT data
Find out how Yalantis set up a seamless process for collecting data from medical IoT devices and ensuring secure data storage with specific time limits for storing only relevant medical data.
Tap into our vast data expertise
Assess the condition of your current data infrastructure and get a custom plan to improve or optimize it.
Our clients’ video reviews
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 data engineering solutions
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 do we perform an architecture assessment process at Yalantis with TOGAF?
Explore the Yalantis architecture assessment process with software architecture assessment templates and examples.
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.
Embark on a streamlined data management journey
Build a new data strategy to meet changing market demands and gain a competitive edge in your industry.
What does data architecture design look like at your data engineering company?
Data architecture is an important part of data infrastructure that enables and governs the processes of exchanging data, storing it, and transferring it from one system to another, as well as how securely data is stored and transferred. Our data engineering services include designing a data architecture based on your business needs, functional and non-functional requirements, and data sources to integrate with. Data architecture design includes setting up data pipelines for ingesting raw data from diverse data sources and transferring it to a data warehouse or data lake for further analysis.
Our data engineers begin with assessing your existing data architecture to see how your data is organized and whether a complete re-architecting is required (or only minor improvements). Based on this initial analysis, we can develop a plan for our work, and after approving it with you, we can begin with the architecture design process.
Which data engineering solutions are the most challenging?
Any data project can have its challenges, and it’s critical to work with an experienced data engineering company to promptly solve them or even avoid them by predicting their occurrence. For instance, setting up an ETL engine can be challenging, as we need to build a data pipeline that works properly with multiple data sources and data types such as structured and unstructured data. Developing seamless real-time data streaming solutions and batch data processing systems can also be a daunting task, especially when it’s necessary to collect data from IoT devices, social media, or legacy software.
The list of data engineering challenges can also include migrating the organization’s data infrastructure to the cloud, setting up data warehouses, and integrating data science solutions such as AI or ML modules.
Is there a difference between a data engineering solution and a data analytics solution?
There is a difference, as data engineering is an umbrella term that covers data aggregation, storage, preparation, and processing, while data analytics is about extracting insights from data that has been fully prepared as the result of data engineering services.
Data engineering service provider can set up a whole data management environment from scratch and it needs to align with your business profile and long-term goals. It’s also critical during data engineering services to perform regular data quality checks to ensure that your data is up-to-date and relevant for the next step: data analysis. Data engineers can work hand in hand with data scientists if your project involves integrating a complex data science solution or you need to add an AI component to your existing data analytics software.
Do your data engineering services differ across industries?
Definitely. Each project is unique, as all industries have different requirements, regulations, data sources, data volumes, and AI needs. For instance, in the healthcare domain, within data engineering services, you may need to collect real-time data from EHR systems, medical IoT devices, and external databases such as Prescription Drug Monitoring Programs (PDMPs) in the USA. Such a variety of data sources requires an efficient and smart approach to data architecture design and strict user authorization to access different types of data.
FinTech and digital banking businesses can deal with constantly increasing data volumes and the need for reliable high-performance data solutions that can withstand high loads and quickly recover in case of failure. Such characteristics require our data engineers to make prompt data technology decisions and timely implement changes that do not disrupt the whole system’s workflow.
Time-tested data engineering solutions and practices at Yalantis
With years of experience in the data engineering realm and a vast team of skilled engineers, data scientists, and consultants, Yalantis can become your guide on the way to a better data management future. A data engineering agency can set up a reliable data infrastructure that helps you increase your operational efficiency, make sense of large data sets, and store only the data you need — and make sure it’s of high quality. Below are some practices and solutions that we bring to the table as a mature data engineering partner.
Data pipeline architecture
Designing and implementing secure and reliable data pipelines is a fundamental aspect of Yalantis’ data engineering services. Our data engineers typically use technologies such as Apache Kafka, Apache Spark, Apache Flink, or cloud-based services like AWS Glue or Google Dataflow to build data pipelines that ingest, transform, and load data from multiple data sources to data warehouses or data lakes. We also can shift your data infrastructure into the cloud or on-premises, or set up a hybrid environment that secures your most sensitive data while allowing for scalable storage of large amounts of data.
Custom data quality framework
We make sure to conduct regular data quality checks and implement end-to-end data validation to ensure data accuracy and reliability, as it’s important that your data infrastructure works with the right data and allows for extracting meaningful insights during the data analytics process. Our data quality framework may involve data profiling, data cleansing, and monitoring data pipelines for anomalies.
Cloud services and continuous integration and continuous deployment (CI/CD)
Our data engineering company also leverages cloud computing platforms such as AWS, Google Cloud, and Microsoft Azure to ensure a scalable and pay-as-you-go data storage environment that allows for quick system deployment and simplifies secure data maintenance. We also implement CI/CD pipelines for automated testing and deployment of data engineering code and configurations to speed up the data engineering process while ensuring it is sufficiently secure.
Data engineering as a service
Yalantis can provide you with data engineering as a service if you want to entrust your entire organization’s data management solely to our data engineers. This approach works for organizations that have no issues sharing most of their data with a third party. As part of this service, we provide companies with the tools, resources, and technologies for comprehensive data infrastructure setup and continual maintenance.