<ul><li>Great Expectations is a popular open-source data quality and testing framework that helps data teams to define, document, and monitor data quality expectations for their datasets.</li><li>Integrating Great Expectations with Databricks allows you to automate data quality checks within your Databricks workflows, ensuring that your data is accurate, consistent, and reliable.</li><li>Great Expectations can be used with a wide variety of data platforms, including relational databases, data warehouses, data lakes, file systems, and big data platforms like Apache Spark and Databricks.</li><li>By following the steps outlined in the article, you can create, validate, save, and load expectations for your data and generate data documentation to visualize the validation results, ensuring data quality and reliability in your data pipelines.</li></ul>

Ensuring Data Quality With Great Expectations and Databricks

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