We are tired of writing custom code for every new source we onboard. How can we design a metadata-driven framework in to automate the ingestion process? I am looking for ideas on how to store source configurations, handle schema changes automatically, and orchestrate these dynamic tasks using tools like Airflow or Prefect to ensure high scalability.
3 answers
A metadata-driven framework starts with a centralized database (like Postgres) where you store source connection strings, table names, and refresh frequencies. Your Airflow DAG should be designed to read this database and dynamically generate tasks using the PythonOperator or TaskGroups. For schema changes, implement a 'schema registry' or use Spark’s mergeSchema. By decoupling the logic from the data configuration, you can onboard hundreds of tables by simply adding a row to your metadata table, which dramatically reduces the manual coding effort and potential for human error during deployment.
How do you handle validation and data quality checks within a dynamic framework like this? If the ingestion is automated, how do we catch bad data before it hits the Gold layer?
This approach is great for scaling. Using Airflow's "Dynamic Task Mapping" feature specifically makes this much easier to implement without writing complex loop-based DAGs.
Spot on. Dynamic Task Mapping is a game changer for this. It allows the scheduler to create a unique task for each entry in your metadata list, providing better visibility and retriability for individual table loads.
You should integrate a tool like Great Expectations or dbt tests into the pipeline. In your metadata table, define the "expectations" for each column (e.g., non-null, specific range). The framework should check these rules after the Bronze layer ingestion. If a check fails, the pipeline should quarantine the data and alert the team rather than proceeding. This ensures that while the process is automated, it still maintains high standards for data integrity.