Extract, Transform, Load (ETL) processes are crucial in data integration, but they can be error-prone. Manual testing of ETL processes is time-consuming and often ineffective. ETL testing automation is essential to ensure data quality and integrity. In this article, we will explore five patterns that can catch 90% of data bugs in ETL testing automation.

In this article:

• Understanding ETL Testing Automation
• Benefits of ETL Testing Automation
• Pattern 1: Data Validation
• Example Code: Data Validation using Python

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Originally published at https://nexmind3.hashnode.dev/etl-testing-automation-5-patterns-that-catch-90-of-data-bugs-1