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Sparkify, a music streaming startup, needs to analyze user listening behavior and understand their music preferences.\n\n## Dataset\n\n* Log Data: JSON logs of user activity (e.g., song plays).\n\n* Song Data: JSON metadata about songs and artists.\n\n## Project Architecture\n\u003cimg src=\"https://github.com/user-attachments/assets/aaad3611-8409-4855-b4f5-10e0e58413ea\" width=\"600\"/\u003e\n\n## Sparkfy Star Schema Database\n\u003cimg src=\"https://github.com/user-attachments/assets/f10df04d-caac-43d6-8234-f7c5b037b730\" width=\"600\"/\u003e\n\n\n## How to Run the Project\n\n- Step 1: Configure AWS Resources\n  * Launch a Redshift cluster and IAM role with S3 read access.\n  * Update the dwh.cfg file with your resources Info.\n\n- Step 2: Set Up the Tables\n  * Run : python create_tables.py\n\n- Step 3: Run the ETL Pipeline\n  * Run: python etl.py\n\n### Repository File Structure\n| File               | Description                                                                    |\n| ------------------ | ------------------------------------------------------------------------------ |\n| `create_tables.py` | Connects to Redshift and creates all necessary tables.                         |\n| `etl.py`           | Runs the ETL pipeline: loads staging tables and inserts into analytics tables. |\n| `sql_queries.py`   | Contains all SQL queries.                      |\n| `dwh.cfg`          | Configuration file with AWS credentials, Redshift, and S3 paths.               |\n\n\n\n\n\n                                        \n\n  \n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpylena%2Fsparkifydatawarehouse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpylena%2Fsparkifydatawarehouse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpylena%2Fsparkifydatawarehouse/lists"}