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End-to-End Data Warehouse & Analytics Project

This project showcases a complete data warehousing and analytics workflow, from raw data ingestion to actionable insights, using SQL Server. Designed as a portfolio-ready project, it highlights practical skills in data engineering, modeling, and business intelligence.


Architecture Overview

The data architecture follows a Medallion approach with three tiers:

Bronze Layer: Stores raw CSV data exactly as it arrives from ERP and CRM sources. Silver Layer: Cleans, standardizes, and prepares the data for analysis. Gold Layer: Transforms data into a star schema for reporting and analytics.


Project Scope

This project demonstrates:

Data Warehouse Construction Implementing a modern SQL Server warehouse with Bronze/Silver/Gold layers. ETL Pipelines Extract, transform, and load data efficiently from multiple sources. Data Modeling Designing fact and dimension tables optimized for analytical queries. Reporting & Analytics Generating SQL-based insights and dashboards for decision-making.


Skills highlighted:

SQL and database design ETL pipeline development Star schema modeling Data analytics & reporting


Tools & Resources

Datasets -CSV files -From ERP and CRM systems

SQL Server Express Lightweight database engine SSMS: GUI for database management Draw.io: For diagrams and architecture design Notion: For project management and documentation GitHub: Version control and code management

Project Requirements

Data Engineering Sources: ERP & CRM CSV files Data Quality: Validate, clean, and normalize the data Integration: Combine sources into a unified analytical model Documentation: Provide clear diagrams and descriptions for stakeholders

Analytics & Reporting

Insights Delivered:

Customer behavior and engagement trends Product performance analysis Sales metrics and trend reports

Objective Enable data-driven decision-making using SQL and dashboards

For more details, see docs/requirements.md

License

This project is licensed under the MIT License. You are free to use, adapt, and share with attribution.

About

An end-to-end data warehouse project demonstrating data extraction, transformation, loading (ETL), modeling, and visualization using SQL, Python, and Power BI. Includes sample datasets, ETL scripts, star schema design, and dashboards to showcase business insights.

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