June 2021
Intermediate to advanced
264 pages
7h 19m
English
Content preview from 97 Things Every Data Engineer Should Know
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Chapter 77. The Three Invaluable Benefits of Open Source for Testing Data Quality
Tom Baeyens
As a software engineer turned data engineer, I can attest to the importance of data in organizations today. I can also attest to the fact that the software engineering principles of writing unit tests and monitoring applications should be applied to data. However, even though people know they should test data, they often don’t have the good practices or knowledge to approach it. Enter open source. In this chapter, I explore three areas in which open source software presents data-testing benefits: for the data engineer, for the enterprise as a whole, and for the tools developers create and use.
Open source tools are developed by engineers, for engineers; if you’re looking to start working with a data-testing tool that will naturally fit into your workflows, you have to start with open source. Open source software can also be embedded into data-engineering pipelines with ease: unencumbered by licensing restrictions or hidden costs, data engineers can be assured that open source tools will get to work quickly and easily.
For the enterprise, open source options have become a great way of getting started with data-quality initiatives. Implementing basic data testing and achieving better data quality in just a few days helps build the business case and starts uncovering data issues, helping ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.