Keeping your data pipelines healthy with the Great Expectations GitHub Action

The blog post, part of a series on using GitHub for MLOps and data science, explores how GitHub Actions can be combined with the open-source project Great Expectations to enhance data pipeline validation within continuous integration (CI) workflows. It emphasizes the importance of data validation for data professionals, like engineers and scientists, who spend significant time on data cleaning and pipeline maintenance. By integrating Great Expectations into GitHub Actions, teams can automate the testing, documentation, and profiling of data, thereby saving time and ensuring data integrity. The post provides a detailed example of a workflow triggered by a SQL query change in a pull request, which demonstrates how failed data validations lead to automatic feedback with links to a data validation dashboard. Great Expectations offers flexibility in creating data expectations either automatically or manually, and connects with various data sources, enhancing its applicability across different platforms. The post concludes by linking to additional resources, encouraging readers to explore further integration possibilities with GitHub Actions in MLOps and data science projects.