https://github.com/bhaveshharmalkar/sql-data-warehouse-project

Comprehensive data warehousing and analytics project, from building a data warehouse to generating actionable insights
https://github.com/bhaveshharmalkar/sql-data-warehouse-project

data-analysis data-analytics data-engineering data-science etl sql sqlserver

Last synced: about 1 hour ago
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Comprehensive data warehousing and analytics project, from building a data warehouse to generating actionable insights

• Host: GitHub
• URL: https://github.com/bhaveshharmalkar/sql-data-warehouse-project
• Owner: bhaveshharmalkar
• License: mit
• Created: 2025-10-17T05:34:04.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2025-11-04T15:05:07.000Z (8 months ago)
• Last Synced: 2025-11-04T16:17:47.986Z (8 months ago)
• Topics: data-analysis, data-analytics, data-engineering, data-science, etl, sql, sqlserver
• Language: TSQL
• Homepage:
• Size: 1.03 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Data Warehouse and Analytics Project

Welcome to the **Data Warehouse and Analytics Project** repository.

This project demonstrates a comprehensive data warehousing and analytics project, from building a data warehouse to generating actionable insights.

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## 🏗️ Data Architecture

The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers

![Data Architecture](https://github.com/bhaveshharmalkar/sql-data-warehouse-project/blob/main/docs/data_architecture.png)

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## 📖 Project Overview

Project Involves:

1. **Data Architecture**: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.

2. **ETL Pipelines**: Extracting, transforming, and loading data from source systems into the warehouse.

3. **Data Modeling**: Developing fact and dimension tables optimized for analytical queries.

4. **Analytics & Reporting**: Creating SQL-based reports and dashboards for actionable insights.

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## 🚀 Project Requirement

### Building the Data Warehouse (Data Engineering)

**Objective**

Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.

**Specifications**

- **Data Sources**: Import data from two source systems (ERP and CRM) provided as CSV files.

- **Data Quality**: Cleanse and resolve data quality issues prior to analysis.

- **Integration**: Combine both sources into a single, user-friendly data model designed for analytical queries.

- **Scope**: Focus on the latest dataset only; historization of data is not required.

- **Documentation**: Provide clear documentation of the data model to support both business stakeholders and analytics teams.

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### BI: Analytics & Reporting (Data Analysis)

**Objective**

Develop SQL-based analytics to deliver detailed insights into:

- **Customer Behavior**

- **Product Performance**

- **Sales Trends**

These insights empower stakeholders with key business metrics, enabling strategic decision-making.