Data validation is crucial for downstream processing of data, as poor data quality can have significant impacts on production systems.
- Pandera and Great Expectations are popular Python libraries for performing data validation.
- Pandera provides an easy way to define validation checks against the columns in your data using a DataFrameSchema, while Great Expectations is a more complex tool that requires creating a Data Context, DataSource, and Expectation Suite.
- Both libraries generate data validation tests automatically and provide the ability to customize these tests.
- Use these tools to ensure that your data meets the expectations of downstream consumers, identify data issues, and maintain data quality.
