Data Engineering Solutions

Create a data architecture and build data pipelines as the foundation of data-driven business

Data Engineering - the very foundation of data-driven insights

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The role of data analytics is more important than ever. The volume, variety, and velocity of data being collected worldwide has exploded in recent years and smart businesses are using it to gain actionable insights, make informed decisions, and gain competitive advantage. In its report ‘Worldwide IT Industry 2018 Predictions’, IDC predicts that 90% of large enterprises will generate revenue from data as a service by 2020. This represents a massive increase from almost 50% in 2017.

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Netflix creates Metacat to combine diverse data sources

Netflix derive their market-leading ability to offer personalized show and movie recommendations to their users from analysis of big data and predictive algorithms. They created Metacat to bring their diverse set of data sources together and to ensure their data platform can interoperate as one ‘single’ data warehouse.

Data Engineering - the foundation of a solid data-driven strategy

As the foundation of a solid data-driven strategy, data engineering generates significant business value. Here are some of the ways your business can benefit from it:

Increase productivity

Data engineers add business value by anticipating what a data scientist needs, and providing them with usable data. This makes data scientists more productive, and in turn, makes processes scalable.

Improve data quality

With data engineering, businesses now have the ability to gather data from a large number of sources, clean it, and validate it before feeding it into analytical systems. This greatly reduces the risk of making misinformed decisions because of missing or inaccurate information.

Reduce costs

As experts in big data technologies, data engineers are best placed to identify the most efficient and effective data architecture and processing pipelines for individual businesses’ needs. This can bring significant cost savings, particularly in regards to storing high volumes of data.

Harness the power of big data

Although data engineering can be valuable when applied to any size data set, it really comes into its own when used in big data analytics. Applications such as machine learning can be leveraged to make cost savings, develop new products and services, and make better decisions based on real-time information.

Starting a Data Engineering project is not easy

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Every organization is unique in its data engineering requirements, which is why it's important to tailor-make intelligent solutions that are capable of scaling with your business.

Any company which depends on high quality information for decision-making can benefit from data engineering and its subsequent application in data science.

Our process

Over the years, our team has tested and implemented a transparent and efficient workflow for Data Engineering projects. The process helps our customers receive more reproducible results faster and in a more flexible way. Our workflow focuses on four stages:

Establish a solid data architecture

Aligning your organization’s data with your business strategies is key to success. We help you establish a sound data architecture which achieves this by providing guidance on how data should be collected, integrated, cleaned, validated, stored, and delivered to the right people, at the right time. We also ensure that your data architecture is able to address a range of issues, such as improving data quality and simplifying data flows.

Implement a scalable data platform

Designed to capture, store, and process large volumes of data, a data platform consists of components such as big data storage, databases and file systems, business intelligence, along with management and administrative tools.

Create processing pipelines

A data pipeline is essentially a set of tools and processes that are used to combine data from a variety of sources into a unified interface that allows users to produce analytics, statistics, and visualizations.

Create long-term strategy

A data-driven approach can help you to create long-term plans and become more dynamic, agile, and profitable. You will be able to create sustainable solutions that can handle the accelerating rate at which new data is being generated and guide you in the development of a successful long-term data as a service-driven strategy.

What is Data Engineering?

In the modern era, data is being generated and stored at an unprecedented rate. In the last two years alone, 90% of the world’s data was created, and the pace is only set to accelerate.

The Internet of Things, social media, web services, mobile devices, transaction data, and databases are among the sources responsible for producing massive amounts of structured and unstructured information, known as big data.

Data science applications are allowing organizations to use big data to take a data-driven approach to solving complex business problems, allowing them to reduce operational costs, create new products and services, and identify new sources of revenue. To do this successfully, they must have access to the right data, in the right format, at the right time.

In most organizations, however, data sets are stored in various formats and rely on different technologies. This is where data engineering provides the solution. While data scientists are concerned with producing insights from a set of data, data engineers focus on getting that data production-ready.

To make data both clear and actionable, it must be cleaned, validated, and prepared for whatever the data scientist is trying to achieve, and allow queries to be run against it. This often means taking a disorganized or unrefined source of data, and converting it into something usable.

Data engineers are also responsible for building and maintaining an organization’s data pipeline. This incorporates everything from gathering the necessary data, processing it, storing it, and enabling access to the end user, whilst taking account of the various technologies and frameworks involved.

Who can benefit from Data Engineering?

A growing number of companies, both large and small, are capturing their data and taking advantage of the insights stored within it. Rapid technological advances have made big data analytics more widely accessible, meaning that any organization which depends on high quality information for decision-making can benefit from data engineering and its subsequent application in data science.

A data-driven approach can help your business become more dynamic, agile, and profitable. From enhancing customer experience with a recommendation engine, to predicting future demand, to detecting anomalies and preventing fraud, the possibilities are endless.

Although becoming mainstream in many areas, data engineering and data science have revolutionized certain industries. In healthcare, organizations are using data to recommend treatment options and make lifesaving diagnoses. The financial services industry is using machine learning to identify and reduce fraudulent transactions, along with advances in anti-money laundering, credit risk management, and regulatory compliance. And in manufacturing, artificial intelligence is being used to increase the efficiency of operations and reduce costs.

World-class specialists at your disposal

Our developers are open source contributors with more than 220 repositories on GitHub.

We constantly invest in developing new technologies and testing various solutions in our R&D department, sharing our experience both on expert blogs and at various tech conferences such as IGARSS, AAIA and MICCAI.

Our team has worked on different engagements, including many end-to-end projects.

Why Choose Our Services?


They might have a team of experts or professionals who are knowledgeable and experienced in their field.

Quality of Service

They could offer high-quality services or products that meet or exceed customer expectations.

Customer Support

Good customer support can be a big factor, including responsiveness, helpfulness, and availability.


Positive reviews, testimonials, or a strong reputation in the industry can inspire confidence in potential customers.

Frequently Asked Questions

Data engineering is the process of designing, building, and maintaining the systems and architectures that enable the collection, storage, processing, and analysis of data.

Data engineers are responsible for developing, constructing, testing, and maintaining architectures such as databases and large-scale processing systems. They also ensure data pipelines are efficient, reliable, and scalable.

Common tools in data engineering include Apache Hadoop, Apache Spark, Apache Kafka, Apache Airflow, SQL databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and cloud services like AWS, Google Cloud Platform, and Azure.

Data engineers should have a strong understanding of programming languages such as Python, Java, or Scala, proficiency in SQL, knowledge of distributed systems and data modeling, experience with ETL (Extract, Transform, Load) processes, and familiarity with big data technologies.

Data engineers ensure data quality by implementing data validation processes, performing data profiling and cleaning, establishing data governance policies, and conducting regular audits of data pipelines.

While data engineers focus on building and maintaining data infrastructure, data scientists analyze data to extract insights and make data-driven decisions. Data engineers prepare and optimize data for analysis, whereas data scientists apply statistical and machine learning techniques to extract actionable insights.

Data engineering solutions can scale with increasing data volume by leveraging distributed computing frameworks, cloud-based storage and processing services, and implementing efficient data partitioning and sharding strategies.