How sabithadharmaraj/ecommerce_etl_project works
This is a Databricks-based e-commerce ETL and warehousing pipeline, run from a single notebook. Its documented workflow extracts 12 CSV datasets from a Unity Catalog volume, cleans them, builds star and snowflake dimensional models, validates the results, and publishes tables and exports to three targets. The README documents the stages and destinations, but no notebook source excerpts were provided, so internal call ownership and finer-grained implementation details cannot be verified.
Main entry points: E-com_ETL_Project.ipynb Star schema
Components 11
ETL pipeline
- Extract datasets
E-com_ETL_Project.ipynb - Clean data
E-com_ETL_Project.ipynb - Validate models
E-com_ETL_Project.ipynb
Dimensional modeling
- Star schema
E-com_ETL_Project.ipynb - Snowflake schema
E-com_ETL_Project.ipynb
Publishing
- Unity Catalog tables
E-com_ETL_Project.ipynb - CSV exports
E-com_ETL_Project.ipynb
Other components
- Databricks user
- Raw CSV datasets
- Unity Catalog volume
- Oracle database
Connections 13
- Databricks user → Extract datasets: runs notebook
README.md - Unity Catalog volume → Raw CSV datasets: contains datasets
README.md - Extract datasets → Unity Catalog volume: loads CSVs
README.md - Extract datasets → Clean data: passes data
README.md - Clean data → Star schema: builds model
README.md - Clean data → Snowflake schema: builds model
README.md - Star schema → Validate models: checks model
README.md - Snowflake schema → Validate models: checks model
README.md - Star schema → Unity Catalog tables: publishes tables
README.md - Star schema → CSV exports: exports schema
README.md - Snowflake schema → CSV exports: exports schema
README.md - Star schema → Oracle database: bulk inserts
README.md - Snowflake schema → Oracle database: bulk inserts
README.md