Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Architectural Logic**: Star schema + cron + backfill form a production batch pattern. **Star Schema**: Fact tables with conformed dimensions; optimizes BI query patterns. **Cron**: `schedule_interval='0 2 * * *'` (2 AM daily); align with source freshness. **Backfill**:...
Architectural Logic: Star schema + cron + backfill form a production batch pattern. Star Schema: Fact tables with conformed dimensions; optimizes BI query patterns. Cron: schedule_interval='0 2 *' (2 AM daily); align with source freshness. Backfill: airflow dags backfill -s start_date -e end_date or TriggerDagRun with execution_date. Why catchup=False: Prevents unintended historical runs; use explicit backfill DAG for controlled replays. Scalability: Idempotent tasks; incremental models where possible; backfill in batches (e.g., 30 days at a time) to avoid resource contention. Cost: Full refresh vs incremental—incremental reduces compute; backfill strategy affects cluster sizing.
Red Flag: Backfilling 2 years of data in one run—saturates cluster, blocks other DAGs. Pro-Move: Chunk backfills (e.g., 7-day batches); use pool limits; monitor queue depth.
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