data-pipelines

Idempotent data pipelines ensure re-runs produce identical results without duplicating data. You can implement idempotency in Airflow using proper task retry configurations, deterministic DAG structures, and safe overwrite patterns during extraction and loading to prevent duplicate records.

Configuring dbt incremental models for large datasets involves using incremental load strategies to process only new or updated records. This approach uses specific materialization configurations and unique key mappings to merge or append data efficiently, reducing warehouse compute costs.

Adding data quality checks to an ETL workflow using Great Expectations involves defining validation rules and expectation suites for your datasets. Integrating these checks within orchestration tools like Airflow or Prefect stops bad data from flowing downstream by failing the pipeline on validation errors.

You can use Prefect instead of Airflow for orchestrating ELT workflows and dbt models. Prefect provides a flexible framework for scheduling and managing task dependencies, and this approach offers production-ready patterns for orchestrating transformations and ensuring reliable pipeline operations.

Non-idempotent ETL jobs cause data corruption by appending duplicate or inconsistent records during pipeline re-runs. You can fix them by implementing idempotent job patterns, utilizing incremental loading strategies, and configuring proper task dependencies and retries in your orchestration tool.