From the course: Data Quality Testing with Great Expectations

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Next steps - Great Expectations Tutorial

From the course: Data Quality Testing with Great Expectations

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In this course, we focused on one core idea. Data quality isn't something you fix after things break. It's something you build into your data pipelines from the start. Using great expectations, we turned that idea into a practical, repeatable workflow for testing data, catching issues early, and maintaining trust in analytics and machine learning systems. The most important takeaway is this. Data quality is not a one-time task. Expectations will evolve as your data evolves and that's normal. The goal isn't to create perfect tests on day one, but to build a system that helps you catch issues early, understand quickly, and respond consistently. Here are some ideas about where to go from here. Pick one real table or data set from your own work and write a small expectation suite. Add a checkpoint to an existing pipeline or a scheduled job and start running validations. Review validation results over time and adjust expectations to keep alerts actionable. Decide on a clear Gx workflow for…

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