Data Pipelines
Extract, Transform, Load (ETL) workflows for moving and processing data at scale.
Components:
- Data sources: APIs, databases, files
- Transformation logic: cleaning, aggregation, feature engineering with Pandas, NumPy
- Storage and staging: data warehouses, S3 buckets, PostgreSQL
- Orchestration: scheduling, error handling, retries
- Monitoring: data quality checks, schema validation
Built with Python scripts, containerized with Docker, scheduled via CD or cloud schedulers (Google Cloud). Core skill for Data Science and backend DevOps.
Related: Python, Pandas, Docker, Data Science, DevOps, Database Design