Data Engineering

Organizations today have an abundance of data, coming from different sources and stored in different ways. It would be convenient to have one access point for all of these, right? 

Data engineering is building systems to allow for the collection and use of data. This data is usually subject to repeated analysis. 

To access structured data, it is possible to choose a Data Warehouse. Typically, copies of the data from the operational source systems are then made overnight and then stored in a particular location where that data is then transformed into the necessary format. That way the production systems in the organization are minimized.

If there is a need to unlock semi- or unstructured data in a cost-effective manner, we opt for a Datalake that meets this objective.

We combine the advantages of a Data Warehouse (analytical infrastructure) and a Datalake (unstructured data & cost-efficient) in a Lakehouse. This implies a different way of working that tracks data in files rather than storing it in tables. Lakehouses offer flexibility in many areas including: data formats, data types, programming capabilities and scalability.

Why is Data Engineering useful?

Any organisation has multiple data sources, systems and applications available. In order to make well informed decisions, information from all these different sources is often required. With the setup of ETL jobs (Extract, Transform, Load), one is able to take the load of production systems and make data and information more easily available to different consumers. By having queryable datasets in place, data can flow through organisations and applications more easily. This allows organisations to do more with their data on a timely basis. Data Engineering sets the basis for any future data initiatives.

Key data engineering tasks

Data ingestion is the process of obtaining and importing data for immediate use or storage in a database. Data can be ingested in batch, near real-time or realtime. The underlying data architecture should facilitate this streaming, CDC, Event-driven or Batch setup.

Data Engineering Tools


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