https://github.com/sugumarsrinivasan/sql-datawarehouse-project
Building Mordern datawarehouse with SQL Server, including ETL Processes, data modeling, and data analytics.
https://github.com/sugumarsrinivasan/sql-datawarehouse-project
data-analysis data-analytics data-engineering data-lake data-science data-warehouse datawarehousing etl etl-pipeline medallion-architecture sql sql-query sql-server
Last synced: 3 months ago
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Building Mordern datawarehouse with SQL Server, including ETL Processes, data modeling, and data analytics.
- Host: GitHub
- URL: https://github.com/sugumarsrinivasan/sql-datawarehouse-project
- Owner: SugumarSrinivasan
- License: mit
- Created: 2025-09-14T13:05:27.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-10-02T13:07:52.000Z (3 months ago)
- Last Synced: 2025-10-02T14:13:58.837Z (3 months ago)
- Topics: data-analysis, data-analytics, data-engineering, data-lake, data-science, data-warehouse, datawarehousing, etl, etl-pipeline, medallion-architecture, sql, sql-query, sql-server
- Language: TSQL
- Homepage:
- Size: 2.55 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Data Warehouse and Analytics Project
Welcome to the **Data Warehouse and Analytics Project** repository! 🚀
This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.
---
## 🏗️ Data Architecture
The data architecture for this project follows Medallion Architecture **Bronze**, **Silver**, and **Gold** layers:
1. **Bronze Layer**: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
2. **Silver Layer**: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
3. **Gold Layer**: Houses business-ready data modeled into a star schema required for reporting and analytics.
---
## 📖 Project Overview
This 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.
🎯 This repository is an excellent resource for professionals and students looking to showcase expertise in:
- SQL Development
- Data Architect
- Data Engineering
- ETL Pipeline Developer
- Data Modeling
- Data Analytics
---
## 🛠️ Important Links & Tools:
Everything is for Free!
- **[Datasets](datasets/):** Access to the project dataset (csv files).
- **[SQL Server Express](https://www.microsoft.com/en-us/sql-server/sql-server-downloads):** Lightweight server for hosting your SQL database.
- **[SQL Server Management Studio (SSMS)](https://learn.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-ver16):** GUI for managing and interacting with databases.
- **[Git Repository](https://github.com/):** Set up a GitHub account and repository to manage, version, and collaborate on your code efficiently.
- **[DrawIO](https://www.drawio.com/):** Design data architecture, models, flows, and diagrams.
- **[Notion](https://www.notion.com/templates/sql-data-warehouse-project):** Get the Project Template from Notion
- **[Notion Project Steps](https://www.notion.so/SQL-Data-Warehouse-Project-26e7a5a375f280f49576d4b6f9cc2f17):** Access to All Project Phases and Tasks.
---
## 🚀 Project Requirements
### 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.
---
### 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.
For more details, refer to [docs/requirements.md](docs/requirements.md).
## 📂 Repository Structure
```
data-warehouse-project/
│
├── datasets/ # Raw datasets used for the project (ERP and CRM data)
│
├── docs/ # Project documentation and architecture details
│ ├── etl.drawio # Draw.io file shows all different techniquies and methods of ETL
│ ├── data_architecture.drawio # Draw.io file shows the project's architecture
│ ├── data_catalog.md # Catalog of datasets, including field descriptions and metadata
│ ├── data_flow.drawio # Draw.io file for the data flow diagram
│ ├── data_models.drawio # Draw.io file for data models (star schema)
│ ├── naming-conventions.md # Consistent naming guidelines for tables, columns, and files
│
├── scripts/ # SQL scripts for ETL and transformations
│ ├── bronze/ # Scripts for extracting and loading raw data
│ ├── silver/ # Scripts for cleaning and transforming data
│ ├── gold/ # Scripts for creating analytical models
│
├── tests/ # Test scripts and quality files
│
├── README.md # Project overview and instructions
├── LICENSE # License information for the repository
├── .gitignore # Files and directories to be ignored by Git
└── requirements.txt # Dependencies and requirements for the project
```
---
## 🛡️ License
This project is licensed under the [MIT License](LICENSE). You are free to use, modify, and share this project with proper attribution.
## 🌟 About Me
Hi there! I'm Sugumar Srinivasan, a Data Engineering enthusiast who loves crafting scalable and reliable data pipelines that transform raw data into actionable insights and help businesses make better decisions. With a strong background in Big Data, Cloud, and DevOps, I enjoy solving complex data challenges, automating workflows, and building secure, efficient, and high-performing data platforms.
Let's stay in touch! Feel free to connect with me on the below Platforms:
[](https://www.linkedin.com/in/sugumarsrinivasan/)