des-airflow-orchestration

Design Airflow DAGs by defining thin, idempotent task boundaries that coordinate phase outputs like dlt ingestion and dbt transformations into executable task graphs, avoiding embedded logic. This approach keeps workflows scheduled, retry-safe, and maintainable.

Implement data quality gates in Airflow by adding pre-publish validation checks using tools like Provero and DuckDB profiling. These gates ensure datasets pass quality validation before release, producing operational evidence for troubleshooting and handoff.

Yes, this Airflow orchestration approach supports integrating dlt for ingestion, dbt for transformations, and DuckDB for profiling within a single DAG. It coordinates these data-stack components while maintaining lineage and alerting plans.

Configure retry and backfill policies in Apache Airflow by applying explicit schedules, timeouts, and catchup behavior to your DAGs. This ensures idempotent reruns, allowing failed tasks to recover safely without duplicating data or side effects.

Lineage and observability planning for Airflow DAG orchestration is needed to track data flow across Bronze to Gold layers and external checks. It provides visibility into task execution, supports alerting, and generates evidence for release readiness.

Yes, you can use Airflow to schedule Bronze to Silver to Gold transformations by structuring DAGs with Provero and dbt validation gates. This coordinates phase outputs, enforces pre-publish checks, and produces evidence packs aligned to release requirements.