A gentle introduction to unit testing, mocking, and patching for beginners
In this talk, I would like to discuss unit testing in data engineering. There are many articles on the internet about Python unit testing, but the topic seems a little vague and unexplored. Learn about data pipelines, their components, and how to test them to ensure continuous delivery. Each step in a data pipeline can be thought of as a function or a process, and ideally should be tested not just as a unit, but all together into a single data flow process. Here’s a list of commonly used techniques for mocking, patching, and testing data pipelines, including integration and automated testing.
What is unit testing in the data world?
Testing is an important part of the software development lifecycle, helping developers check the reliability of their code and ensure that it can be easily maintained in the future. Think of a data pipeline as a series of processing steps or functions. In this case, unit testing can be thought of as a technique for writing tests to ensure that each unit of code or each step of a data pipeline does not produce unintended results and is fit for purpose.
In a nutshell, each step in your data pipeline is a method or function that you need to test.
Your data pipeline may be different. In fact, they often differ significantly in terms of data sources, processing steps, and final destination of the data. Whenever you transform data from point A to point B, a data pipeline exists.There are various design patterns [1] I wrote about techniques for building these data processing graphs in a previous article.
Take a look at the simple data pipeline example below. Demonstrates common use case scenarios when data is processed in multicloud.Our data pipeline starts with…
