📦 Create Data Warehouse Project

This project is an end-to-end data engineering solution that extracts sales data through an ETL (Extract-Transform-Load) process, loads it into an MSSQL data warehouse modeled using a dimensional (Kimball) architecture, and visualizes it with Power BI.

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🛠️ Technologies Used

• Python (pandas, pyodbc, dotenv) – ETL operations
• MSSQL – Data warehouse platform
• Kimball Model – Dimensional data modeling
• Power BI – Data visualization
• VS Code – Development environment

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🧱 Project Structure

• create-datawarehouse/
• ├── seed_data/ – Realistic CSV datasets
• │ ├── customers.csv
• │ ├── products.csv
• │ └── orders.csv
• ├── etl_pipeline.py – Handles MSSQL connection, data load, and error logging
• ├── schema.sql – Data warehouse schema (Dim and Fact tables)
• ├── procedures.py – Simulated stored procedures
• ├── analysis_queries.sql – OLAP-style queries (slicer, filter, trend)
• ├── test_pipeline.py – ETL test and data validation
• ├── etl_errors.log – Logs data quality issues
• ├── .env – MSSQL connection credentials
• ├── requirements.txt – Python dependencies
• └── README.md

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🧮 Data Warehouse Model (Kimball Approach)

Fact Table	Dimension Tables
FactOrder	DimCustomer, DimProduct, DimCategory, DimDate

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⚙️ ETL Workflow

• MSSQL connection is established using credentials from .env
• Run etl_pipeline.py with Python
• CSV datasets are loaded into:
  • DimCustomer
  • DimCategory & DimProduct
  • FactOrder
• Data quality issues are logged into etl_errors.log
• ETL tests and validation are run using test_pipeline.py

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📊 Power BI Dashboard – Sales Analysis

The dashboard below visualizes the post-ETL data warehouse model using Power BI.

📸 Dashboard Preview

📈 Visuals Included:

• Bar Chart → Top ordering customers
• Pie Chart → Order distribution by city
• Line Chart → Monthly order trend
• Stacked Column Chart → Sales by product category