https://github.com/prakashpandey16/sql_data_warehouse_project
Building a modern data warehouse with SQL Server, including ETL Processes, data modeling, and analytics.
https://github.com/prakashpandey16/sql_data_warehouse_project
cleaning-data data data-engineering data-science database etl-pipeline sqlserver
Last synced: 6 months ago
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Building a modern data warehouse with SQL Server, including ETL Processes, data modeling, and analytics.
- Host: GitHub
- URL: https://github.com/prakashpandey16/sql_data_warehouse_project
- Owner: prakashpandey16
- License: mit
- Created: 2025-04-11T11:11:07.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-11T12:07:53.000Z (6 months ago)
- Last Synced: 2025-04-11T13:49:01.761Z (6 months ago)
- Topics: cleaning-data, data, data-engineering, data-science, database, etl-pipeline, sqlserver
- Language: TSQL
- Homepage:
- Size: 9.66 MB
- Stars: 0
- Watchers: 1
- 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.
---
## 🛠️ 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 Template](https://www.notion.com/templates/sql-data-warehouse-project):** Get a customizable project planning template.
- **[Notion Project Workspace](https://www.notion.so/SQL-Data-Warehouse-Project-1cc8955f9e4380928e7adb64f38d3c85):** Full access to project tasks, progress tracking, and documentation in Notion.
---
## 🚀 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.
---
## 📂 Repository Structure
```plaintext
data-warehouse-project/
│
├── datasets/ # Raw datasets used for the project (ERP and CRM data)
│
├── docs/ # Project documentation and architecture visuals
│ ├── Data_Flow.png # Visual representation of data flow
│ ├── Data_integrations.png # Diagram of different data integrations
│ ├── Data_mart.png # Schema or design of data marts
│ ├── ETL_modal.png # Visual explanation of ETL processes
│ ├── High_level_architecture.png # Overview of the system architecture
│ ├── data_catalog.md # Catalog of datasets, including field descriptions and metadata
│ ├── naming_conventions.md # Guidelines for consistent naming of 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 data quality validation
│
├── README.md # Project overview, setup instructions, and usage guide
├── LICENSE # License information for the repository
├── .gitignore # Files and directories to be ignored by Git
```
---
## 🛡️ 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
I'm Prakash Pandey, a BCA student who is passionate about Data Engineering, building real-world data solutions, and solving business problems with technology.
- 🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/prakash-pandey-884590263/)