3.7 Power ups

Power ups are specialized integrations that connect Mage with external data engineering tools like Great Expectations, monitoring systems, and analytics frameworks. They allow you to leverage existing tool investments while maintaining a unified pipeline experience, handling authentication and configuration complexity behind a consistent Mage interface.

Extensions - Great Expectations

Great Expectations extensions brings enterprise-grade data validation and quality monitoring directly into your Mage pipelines. This integration allows you to define, execute, and monitor data quality expectations without leaving the Mage environment.

Understanding Great Expectations integration

When you add a Great Expectations power up to your pipeline, Mage automatically handles the connection between your data processing blocks and Great Expectations validation suites. The integration manages expectation execution, result collection, and failure handling as part of your standard pipeline flow.

Key features of the integration

Automatic validation execution: Great Expectations suites run automatically after data transformation blocks complete, ensuring data quality checks happen consistently without manual intervention.

Native result handling: Validation results integrate with Mage's monitoring and alerting systems, so data quality failures can trigger the same notification workflows as other pipeline failures.

Configuration management: Store and manage Great Expectations configurations alongside your pipeline code, maintaining version control and environment consistency.

Checkpoint integration: Existing Great Expectations checkpoints work seamlessly within Mage pipelines, preserving your current data validation investments.

Conclusion

Power ups transform Mage from a pipeline orchestration tool into a comprehensive data platform that integrates seamlessly with your existing data engineering stack. The Great Expectations integration exemplifies this approach by bringing enterprise data quality capabilities directly into your pipeline workflows, eliminating the operational overhead of managing separate validation systems while maintaining the robustness and flexibility that data quality frameworks provide.