The lost art of data engineering 1: a data engineer's chronicles

The lost art of data engineering 1: a data engineer's chronicles by Ara Islam

This series is aimed to help you take a step back and ground yourself on the core principles that every data team must understand. We will focus on the data engineering process, which is the art of preparing the most valuable ingredient your company has at its disposal: your data.

In today's fast-paced world of data, companies are racing to leverage the latest trends and innovations to gain a competitive edge. With AI most recently taking centre stage as the key buzzword and topic of every C-suite board meeting. It's no wonder if you are new to the data space, you might struggle to see past the spider webs of fancy terms and exotic ideas that seem to appear almost on a daily basis.

For any company to use the latest and greatest instruments of competitive edge like AI chat bots or Business Intelligence (BI) Reports, is to have a sophisticated backend structure. One that allows your data to bend to your needs while staying organised, accurate and governed. 

But before we can get into some of the topics of data engineering, it's important to first define what a data engineer is. A Data Engineer is a person who designs, builds, and maintains data systems. This definition can overlap with responsibilities of the other roles within a data team like Data Architects who also work on part of the design. A Data Architect may join a project before a data engineer and master the process and infrastructure. However, a Data Engineer's input is essential to the designing of how data transforms and moves across systems. 

A Business Analyst may be asked to build a report to aid in commercial decision making. But, as the demand for reports increases and they become more critical, an analyst alone isn't enough. They would have to sacrifice on accuracy or time to deliver. Both of which can affect a business trust in data and the effectiveness of decisions.

That is why a Data Engineer is essential in data driven transformations. They enable Data Analysts, Data Scientists and ML Engineers to focus on building reports and models quickly  with great certainty.

Throughout this series, we will touch on core principles of Data Engineering. Whether you are a Data Engineer yourself or member of a data team, the aim is to  gain a deeper appreciation for the craft, and understand why it's so important.

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