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Automated ETL Pipeline
A scalable data pipeline that extracts, transforms, and loads e-commerce data into a data warehouse for analytics.
November 20, 2023completed
PythonApache AirflowPostgreSQLdbtDockerpandas
Overview
Built an automated ETL pipeline that processes raw e-commerce transaction data, cleans and transforms it, and loads it into a PostgreSQL data warehouse — enabling business intelligence dashboards.
Pipeline Architecture
S3 Raw Data → Airflow DAG → Python Transform → dbt Models → PostgreSQL DWH → Metabase
Key Features
- Idempotent runs: Safe to re-run without duplicate data
- Data quality checks: Automated tests using dbt's built-in testing framework
- Error alerting: Slack notifications on pipeline failures
- Incremental loads: Only processes new/changed records
Results
- Reduced report generation time from 4 hours to 15 minutes
- Processes ~2M rows/day reliably
- Data freshness SLA: < 1 hour end-to-end