https://github.com/yrehim7/data_warehouse_project
A complete, easy-to-follow guide on building a modern data warehouse with SQL Server. Learn how to design ETL processes, create effective data models, and leverage analytics for better insights.
https://github.com/yrehim7/data_warehouse_project
data-cleaning data-lakehouse database datawarehouse datawarehousing etl medallion-architecture sql sql-query sql-server
Last synced: 10 months ago
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A complete, easy-to-follow guide on building a modern data warehouse with SQL Server. Learn how to design ETL processes, create effective data models, and leverage analytics for better insights.
- Host: GitHub
- URL: https://github.com/yrehim7/data_warehouse_project
- Owner: yrehim7
- Created: 2025-02-13T00:57:07.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-04-09T14:18:14.000Z (10 months ago)
- Last Synced: 2025-04-09T15:31:15.122Z (10 months ago)
- Topics: data-cleaning, data-lakehouse, database, datawarehouse, datawarehousing, etl, medallion-architecture, sql, sql-query, sql-server
- Language: TSQL
- Homepage:
- Size: 1.54 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 🗃️ Data-Warehouse Project
This project showcase an complete data warehousing and analytics solution from building a data warehouse to extract meaningful insights. Designed as industry best practices in data engineering and analytics
## Data Architecture
This project follows the Medallion Architecture, organizing data into three layers:
1. **Bronze** Layer: Stores raw data directly from source systems. In this project, data is ingested from CSV files into a SQL Server database..
2. **Silver Layer**: Performs data cleansing, standardization, and normalization, ensuring the data is structured and ready for analysis.
3. **Gold Layer**: Contains business-ready data modeled into a star schema, optimized 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.
SQL Development
Data Architect
Data Engineering
ETL Pipeline Developer
Data Modeling
Data Analytics
## 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 Pipeline Overview ####
- **Data Sources**: Import data from two source systems (**ERP** and **CRM**) provided as CSV files.
- **Data Quality**: Cleanse and resolve data quality issues before analysis.
- **Integration**: Combine both sources into a single, user-friendly data model optimized 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.
## License
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.