SQLite to SQL Server ETL pipeline using Python orchestrates data extraction, transformation, and loading across three SQL Server layers—raw, curated, and aggregated—before serving insights to Power BI dashboards. The Extract stage uses sqlite3 to read from source.db, Transform applies pandas for data cleaning and enrichment, and Load leverages pyodbc/SQLAlchemy to populate the raw layer with CSV and JSON files. Data flows through the curated layer for business logic and aggregated layer for dimensional marts, enabling self-service analytics. Fork this diagram to customize table schemas, add scheduling logic, or integrate Apache Airflow for production orchestration.