📊 Data Warehouse & Analytics Project
Welcome to my Data Warehouse and Analytics Project repository! 🚀 This project showcases an end-to-end data warehousing solution — from raw data ingestion to analytical reporting. It is designed as a hands-on portfolio project demonstrating practical data engineering and analytics skills using SQL Server.
🏗️ Data Architecture
This project follows the Medallion Architecture pattern with Bronze, Silver, and Gold layers.
🔹 Bronze Layer
Stores raw data exactly as received from source systems. Data is ingested from CSV files into SQL Server without transformation.
🔹 Silver Layer
Performs data cleansing, validation, standardization, and transformation to make the data analysis-ready.
🔹 Gold Layer
Contains business-ready data modeled into a star schema, optimized for reporting and analytics.
📖 Project Overview
This project demonstrates:
Designing a modern data warehouse using layered architecture
Building ETL pipelines using SQL
Performing data cleaning and transformation
Creating fact and dimension tables for analytics
Writing analytical SQL queries for business insights
🎯 This project highlights my skills in:
SQL Development
Data Warehousing
Data Modeling
ETL & Data Transformation
Data Analytics
🛠️ Tools & Technologies Used Tool Purpose SQL Server Express Database engine for the warehouse SQL Server Management Studio (SSMS) Database development & querying CSV Files Source data (ERP & CRM systems) Draw.io Data architecture and model diagrams Git & GitHub Version control and project hosting 🚀 Project Requirements 🏗️ Building the Data Warehouse (Data Engineering)
Objective: Develop a structured data warehouse that consolidates sales data from multiple systems for reporting and analysis.
Key Tasks:
Import data from ERP and CRM CSV sources
Clean and validate data to resolve quality issues
Transform raw data into structured, analysis-ready tables
Design a star schema for reporting
Document the data model clearly for business and analytics users
📊 Analytics & Reporting (Data Analysis)
Objective: Use SQL queries to generate business insights related to:
Customer Behavior
Product Performance
Sales Trends
These insights help support data-driven decision-making.
More details can be found in: 📄 docs/requirements.md
📂 Repository Structure data-warehouse-project/ │ ├── datasets/ # Raw ERP and CRM datasets (CSV files) │ ├── docs/ # Project documentation & architecture │ ├── data_architecture.drawio │ ├── data_flow.drawio │ ├── data_models.drawio │ ├── data_catalog.md │ ├── naming-conventions.md │ ├── scripts/ # SQL scripts for each warehouse layer │ ├── bronze/ # Raw data loading scripts │ ├── silver/ # Data cleaning & transformation scripts │ ├── gold/ # Analytical model creation scripts │ ├── tests/ # Data quality and validation queries │ ├── README.md # Project documentation ├── LICENSE # License information └── .gitignore # Files ignored by Git
🧠 Key Learning Outcomes
Through this project, I practiced:
✔ Designing layered data architecture ✔ Writing SQL transformations for data cleansing ✔ Handling data quality issues (NULLs, invalid dates, incorrect calculations) ✔ Building fact and dimension tables ✔ Creating analysis-ready datasets for reporting
👨💻 About Me
Sai Kumar Embadi | Data Engineer with Analytics Expertise Building data pipelines, transforming raw data, and enabling insights for smarter business decisions.
This project is part of my journey in strengthening my skills in:
SQL & Data Warehousing
Data Modeling
Analytics & Reporting
🔗 Connect With Me
🛡️ License
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.