Airflow empowers data pipelines with the ability to ensure data quality throughout the workflow. This verification process can be implemented at various stages, safeguarding data integrity before transformations and after it lands in its final destination. Airflow offers built-in operators like SQLCheckOperator, enabling you to craft data quality checks using familiar SQL queries. Alternatively, external frameworks like Great Expectations can be seamlessly integrated to define intricate data validation rules in JSON format. These checks can target a broad spectrum of data characteristics, including null value counts, adherence to specific value ranges, and expected row counts.

By strategically incorporating data verification throughout your Airflow DAGs, you can proactively identify and address data quality anomalies, fostering trust in the data products your pipelines generate. This not only saves time by preventing downstream errors but also instills confidence in data-driven decisions made by stakeholders across the organization.

Integration with Airflow allows you to verify data through Secoda. An Automation consists of Triggers and Actions. Triggers activate the workflow based on specific schedules, such as hourly, daily, or custom intervals. Actions encompass various operations like filtering and updating metadata. You can stack multiple actions to create customized workflows for your team's requirements. Secoda enables bulk updates to metadata in Airflow.

Secoda's integration with Airflow allows users to verify data through the platform. By consolidating data catalog, lineage, documentation, and monitoring, Secoda serves as a comprehensive data management platform. With its AI data governance capabilities, Secoda seamlessly integrates with Airflow, providing users with a reliable solution for data verification.