Data Engineering

Build future-proof data platforms with the power of

Data engineering

Data Engineering helps you in collecting and validating your data

Data engineering focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information.

Data Engineers are there where the rubber meets the road, focusing on the applications and collecting of Big Data. Through data engineering, mechanisms are put in place to apply Big Data into real-world operations.

How can data engineering help you?

distributed processing

Set up a distributed processing framework with Apache Spark enabling you to split processes and run them in parallel

stream processing

Build a continuous processing framework with Apache Kafka for the processing of real-time data as it is produced and received.

job processing

Execute processing jobs by orchestrating them between nodes and executors to complete complicated analytical workloads

Scheduling & orchestration

Schedule jobs in your data pipeline and manage dependencies and errors with Apache NiFi as a wider dataflow solution

data governance

Build a data governance program to focus on data definitions and standards, quality, security, privacy, architecture and integration

cloud data platforms

Leverage the flexibility of AWS and Azure Cloud to build fully interoperable, cost-effective and expansive data platforms

hybrid cloud solutions

Keep your on-premise data infrastructure and mix it with the computing and storage services of AWS and Azure Cloud

etl pipelines

Build ETL pipelines to extract data from an input source, transform the data and load it into an output destination

Test automation

Build automated tests in your data pipelines to validate data flows and catch costly production errors

This is just a grasp out of many possible fields where Data Engineering can offer tremendous value.

Curious what possibilities lie in your organization?

data engineering technology stack

Over the past few years, many new technologies have made their advances in the data processing world. At Infofarm, we use these technologies to build highly performant and scalable data platforms for our partners.

Data platform components

Building a future-proof and sustainable data platform is a complex task, involving a bunch of important components we need to carefully take into account.


Get your data into the platform from various sources


Store data in a future-proof and efficient manner


Generate a clean and uniform data format


Think of a good way of accessing your data from various applications


Process and combine your data for different use cases


Monitor the operations and performance of your platform


Ensure the stability and durability of your data


Adhere to regulatory privacy requirements


Make sure everybody knows where the data is coming from and what it means


Ensure your data is being stored in a secure way


Orchestrate all your data processes in an automated way


Archive the data that is less frequently used


Visualize and explore data in your platform

How can we help you?

Do you want to start leveraging value from your data by using Data Engineering techniques? We can support you in every step of the data-driven value chain.

Data Workshops

Brainstorm sessions to come up with valuable use cases and a step-by-step implementation roadmap.


On-site support through consultancy and knowledge sharing for your teams during your Data Engineering projects.

Custom development

Agile development of data-driven prototypes, full-scale applications and scalable data platforms.


Training courses to educate your teams on the main principles and concepts of Data Engineering.