Apache Airflow
What it is: Workflow orchestration platform. Schedule and monitor data pipelines defined as Python DAGs.
What It Does Best
DAGs in Python. Define workflows as code. Version control, testing, reusability.
Rich UI. See pipeline status, logs, task dependencies. Debug failures visually.
Retry and alerting. Automatic retries, custom failure alerts, SLA monitoring.
Pricing
Free. Open source, Apache 2.0 license.
Managed options: Google Cloud Composer ($300+/month), AWS MWAA ($400+/month)
When to Use It
✅ Complex data pipelines with dependencies
✅ Scheduled ETL jobs
✅ Need monitoring and alerting
✅ Team knows Python
When NOT to Use It
❌ Real-time streaming (use Kafka, Spark Streaming)
❌ Simple cron jobs (cron is simpler)
❌ Event-driven workflows (use event processors)
Bottom line: Industry standard for batch data pipelines. Setup complexity pays off once you have multiple dependent jobs. Essential for data engineering teams.