Engineering is the science of designing and building machines, objects or structures. Likewise, data engineering is the science of designing and building data pipelines. Its aim is to create the best possible infrastructure, one that would empower Data Scientists and Analysts to work optimally by making raw data easily available and actionable for everyone.
Data engineering is – fittingly – the turf of Data Engineers. Their job is to design, build and maintain data systems (or pipelines) as well as troubleshoot any issue in the systems involved. This enables the rest of the data team to store, process and analyze large datasets efficiently.
A well-constructed data pipeline must collect data from various sources and load it into a single data warehouse. As a result, miscellaneous data can be displayed evenly, and the data warehouse becomes a Single Source Of Truth (SSOT).
Let’s rewind a little, shall we? Over the last decade, most companies have experienced an irreversible and life-changing digital transformation. They ended up with huge amounts of data, and Data Scientists proved essential to help them make sense of all this.
However, organizations weren’t yet aware that the data needed to be organized, nor that it was necessary to ensure its quality, security and availability. Data Scientists were expected to build the infrastructure and data pipelines themselves before they could even start doing their actual job. Problem is, this operation often wasn’t part of their skillset – and data modeling would therefore wound up inaccurate.
This is when and why data engineering quickly made itself indispensable!
Because without data engineering, companies would find themselves overflowing with non-actionable – and therefore useless – data.
Data engineering has now become a must-have for any organization, and there are two main reasons for this:
A Data Engineer’s role covers 4 main areas of intervention:
They must not only master many technical hard skills, but also have a good understanding of what the organization needs and how Business Users make use of the data available.