Summary

Testing strategies for data pipelines are crucial for maintaining data integrity and pipeline efficiency in any data-centric organization. Given the diverse potential issues arising from source data, transformational bugs, or infrastructure problems, robust testing measures are indispensable. With the right approach, you can ensure the reliability and integrity of your data pipelines. It is likely that a combination of these different types of testing, tailored to the specific requirements and constraints of your pipeline, will significantly contribute to your organization’s data-driven success.

Continuous monitoring is part of the testing strategy. In the next chapter, we’ll explore important metrics for tracking your pipeline health, such as latency, error rates, and data quality indicators, as well as various logging strategies that empower you to create a pipeline that is not only robust but also easy to debug when errors inevitably arise in the future...