In this story, I would like to raise a discussion on unit testing in data engineering. Although there are many articles about Python unit testing on the Internet, the topic seems a bit vague and discovered. We'll talk about data channels, the parts that make them up, and how we can test them to ensure continuous delivery. Each data flow step can be considered as a function or process and should ideally be tested not just as a unit but all together, integrated into a single data flow process. I'll try to summarize the techniques I often use to simulate, patch, and test data pipelines, including integration and automated testing.
What are unit tests in the world of data?
Testing is a crucial part of any software development lifecycle and helps developers ensure that the code is reliable and can be easily maintained in the future. Consider our data pipeline as a set of processing steps or functions. In this case, unit testing can be thought of as a test writing technique to ensure that each unit of our code or each step of our data pipeline does not produce unwanted results and is fit for purpose.
Simply put, each step in a data pipeline is a method or function that needs to be tested.
Data pipelines can be different. In fact, they tend to vary greatly in terms of data sources, processing steps, and final destinations of our data. Whenever we transform data from point A to point B, there is a data pipeline. There are different design patterns (1) and techniques to build these data processing graphs and I wrote about it in one of my previous articles.
Take a look at this simple data pipeline example below. Demonstrates a common use case scenario when data is processed across multiple clouds. Our data flow starts from…
