In this project, I built a fully automated data pipeline for Sparkify using Apache Airflow to orchestrate the ingestion, transformation, and loading of large-scale music streaming data into a cloud data warehouse on Amazon Redshift. The goal was to enable reliable analytics on user activity and song listening patterns by converting raw, semi-structured JSON data into a clean and structured dimensional model.
The pipeline begins by extracting user event logs and song metadata stored in Amazon S3 and staging them into Redshift. From there, I designed and implemented modular ETL workflows that transform the raw data into fact and dimension tables optimized for analytical queries. These workflows are built using reusable custom Airflow operators, allowing each stage of the pipeline—staging, loading, and transformation—to be independently managed and reused across tasks.
To ensure production-grade reliability, the pipeline includes automated data quality checks that validate table completeness and data consistency after each ETL run. This helps detect missing or incorrect records early and maintains trust in downstream analytics.
Overall, the project demonstrates key data engineering concepts such as workflow orchestration, scalable ETL design, cloud data warehousing, and data quality assurance, while showcasing how Apache Airflow can be used to build maintainable and monitorable data pipelines in a real-world environment.
