Building ETL Pipelines with Python

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Testing Strategies for ETL Pipelines

The main purpose of data pipelines is to facilitate the movement of information from its source to its destination. There is strength in this simplicity. But as we’ve seen throughout this book, pipelines have far more complexity under the hood, and this makes them equally prone to errors.

We’ve talked about how errors may arise from source data anomalies, transformation bugs, infrastructure hiccups, or a host of other reasons, but we haven’t taken a deep dive into the structural components that data engineers can add to their pipeline ecosystem to ensure data integrity, reliability, and accuracy throughout the pipeline.

Testing data pipelines isn’t a one-size-fits-all process, but it can certainly be a ...