🎬 Netflix Data Warehouse Pipeline using Snowflake, dbt & BI Tools

This project demonstrates an end-to-end modern data pipeline to transform Netflix CSV data into analytics-ready tables using Amazon S3, Snowflake, and dbt, with visualizations powered by Power BI, Looker Studio, and Tableau. It follows a layered architecture with raw, staging, and serving layers to ensure clean, testable, and modular data transformations.

🔧 Tech Stack

• Amazon S3 for raw data storage
• Snowflake as the cloud data warehouse
• dbt (Data Build Tool) for transformation, testing, and orchestration
• Power BI, Looker Studio, Tableau for business intelligence and reporting
• CSV as the initial input format

⚙️ How It Works

• Netflix data in CSV format is extracted and loaded into Amazon S3.
• Data is ingested from S3 into Snowflake's raw layer.
• dbt transforms the data across layers:
  • Raw → Staging → Serving (Dev)
  • Enables testing, modularization, and documentation of models.
• Transformed data in the serving layer is visualized using Power BI, Looker Studio, and Tableau for business insights.

📌 Key Highlights

• Designed a modular ELT pipeline using Snowflake and dbt with layered architecture.
• Implemented data transformation, testing, and orchestration using dbt best practices.
• Enabled interactive dashboards in Power BI, Tableau, and Looker Studio from the final serving layer.
• Demonstrated a scalable and maintainable data warehouse model for analytical use cases.