The Great Expectations (GX) Airflow provider, maintained by Astronomer, changes how organizations approach data quality by embedding validation directly within Airflow DAGs. This integration allows data teams to validate data at any point in their pipelines, preventing quality issues from propagating downstream and ensuring trustworthy data for analysis and decision-making.
Why integrate data validation into your Airflow workflows?
Embedding GX validations into Airflow pipelines delivers multiple advantages:
Improved data quality: data quality checks become an integral component of your ETL/ELT workflows, automatically verifying data meets expectations before proceeding to the next task.
Integration: choose from three distinct operators tailored to different validation scenarios, whether you're working with in-memory DataFrames or data stored in external systems.
Scalability: validate datasets of any size– from in-memory data frames to tables with petabytes of data, with consistent performance.
Enhanced debugging and issue identification: configure alerts and trigger follow-up actions based on validation success or failure, enabling proactive data quality testing rather than reactive troubleshooting.
Choosing the right operator
Your operator choice should be based on three key factors: where your data lives, what actions you need to trigger after validation, and how you track validation history.
Best for: Validating in-memory DataFrames (Pandas or Spark)
This lightweight operator provides a streamlined approach for validating data already loaded into memory. Simply provide a dataframe and an Expectation Suite or Expectation to generate validation results immediately within your pipeline.
Best for: Validating data stored in external systems
When your data resides in databases, data lakes, or warehouses, this operator allows you to define a BatchDefinition to validate the data against your Expectations or an Expectation Suite without loading the entire dataset into memory.
Best for: Comprehensive validation with follow-up actions
The most powerful option in the toolkit, this operator supports all GX Core features and enables you to define a complete validation workflow using Checkpoints, BatchDefinitions, ExpectationSuites, and ValidationDefinitions. Use this when you need to trigger alerts, generate reports, or initiate downstream processing based on validation results.
Persistence and Historical Tracking
Need to track validation results over time for trend analysis or audit purposes? Configure a file-based or cloud-backed Data Context to persist your validation history, enabling long-term quality insights and performance tracking.
Get started
Ready to enhance your data pipeline with built-in quality checks?
Install GX Provider 1.0.0a1 with pip: pip install great-expectations-provider==1.0.0a1
Explore the documentation to see how it fits into your workflows.
Why it matters
With the GX Airflow provider, a collaboration between GX and Astronomer, data teams transform data quality from an afterthought to a foundational element of their data architecture. Each pipeline becomes self-validating, and self-reporting, ensuring that only good data flows downstream. Incorporating GX into your Airflow data pipelines not only helps maintain high-quality data but also fosters transparency, reliability, and efficiency within your data operations, enabling your business to make decisions with confidence.
Take action
Install the provider: pip install great-expectations-provider==1.0.0a1
Join our community to connect with other data practitioners implementing validation in their workflows
Schedule a demo: See how leading organizations have implemented GX Cloud with Airflow
Deploy your Airflow pipelines into production using a free trial of Astro
Don't let data quality issues undermine your analytics. Implement automated validation with the GX Airflow provider and ensure your organization’s decisions are always based on trusted data.
Migrate to GX Core 1.0.x today
