5 Biggest Data Engineering Mistakes and How to Avoid Them

Note: I have received no compensation for writing this piece. Please consider supporting mine and others’ writing by becoming a Medium member with this link.

If you’re anything like most data professionals, you’ve probably learned the best practices on the go during the job. You’ve most likely made some questionable decisions while building your data stack. After all, we’re human and make mistakes. From building unmaintainable systems to not communicating enough with business stakeholders. This article provides a list of the most common mistakes data engineers make to help you avoid the same pitfalls.

What do Data Engineers do?

Data engineers are the ones who build, maintain, and improve data pipelines. They are the people who ensure that all the software and hardware work together to create a smooth flow of data.

Building Unmaintainable systems

One of the most common mistakes data engineers make is building unmaintainable systems. ETL solutions and data warehouses that rely too much on complicated code and can’t be handled without the original data engineer’s contribution are unsustainable and, in the end, inefficient. With so many new technologies and tools, it can be difficult to know what will work best…