SQL Data Warehouse Project

📌 Project Overview

This project demonstrates the design and implementation of a Data Warehouse using Microsoft SQL Server.
It includes ETL (Extract, Transform, Load) processes, basic data transformations, and loading data into a structured data warehouse for analytics and reporting.

This project is created to showcase practical knowledge of data warehousing concepts and SQL-based ETL workflows.

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🏗️ Architecture

• Source Data (Raw tables / CSV files)
• Staging Tables
• Data Transformation Layer
• Data Warehouse (Fact and Dimension Tables)

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🧰 Technologies Used

• Microsoft SQL Server
• T-SQL
• SQL Server Management Studio (SSMS)
• GitHub

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🔄 ETL Process

1. Extract

• Data is extracted from source tables or flat files
• Raw data is loaded into staging tables

2. Transform

• Handling NULL values
• Removing duplicates
• Data type conversions
• Standardizing values
• Applying basic business rules

3. Load

• Data is loaded into dimension tables
• Fact tables are populated using surrogate keys
• Relationships are maintained between fact and dimension tables

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🗃️ Data Warehouse Design

• Fact Tables

  • Store business metrics and measures

• Dimension Tables

  • Store descriptive attributes like date, product, customer, etc.

• Schema used:

  • Star Schema / Snowflake Schema (based on requirement)

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