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🏗️ SQL Data Warehouse Project

Building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics — a hands-on portfolio project demonstrating core data engineering fundamentals.


📖 Overview

This project implements an end-to-end data warehouse using the Medallion Architecture (Bronze → Silver → Gold), consolidating raw source data into clean, analytics-ready models. It covers the full pipeline: ingesting raw data, cleaning and standardizing it, and modeling it into a star schema for reporting.

🏛️ Data Architecture

Layer Purpose
Bronze Stores raw, unmodified data ingested directly from source files into SQL Server.
Silver Cleans, standardizes, and normalizes data — handling nulls, duplicates, and inconsistent formats.
Gold Business-ready data modeled into a star schema (fact and dimension tables) for reporting and analytics.

📂 Repository Structure

sql-data-warehouse-project/ │ ├── datasets/ # Raw source data used for the project ├── scripts/ # SQL scripts organized by layer │ ├── bronze/ # Scripts to create and load raw tables │ ├── silver/ # Scripts to clean and transform data │ └── gold/ # Scripts to build the star schema (facts & dimensions) ├── tests/ # Data quality and validation scripts ├── LICENSE # MIT license └── README.md # Project overview and instructions

⚙️ Tech Stack

  • Database: Microsoft SQL Server
  • Language: T-SQL
  • Concepts applied: Stored procedures, surrogate keys, star vs. snowflake schema design, ETL pipelines, data quality validation

🚀 Getting Started

  1. Install SQL Server (2019+) and SSMS.
  2. Clone this repository.
  3. Run the scripts in scripts/bronze/ to create and load raw tables.
  4. Run the scripts in scripts/silver/ to clean and transform the data.
  5. Run the scripts in scripts/gold/ to build the final star schema.
  6. Run the scripts in tests/ to validate data quality across layers.

✅ Data Quality Checks

The tests/ folder contains validation scripts checking for duplicate keys, null values in critical fields, and referential integrity between fact and dimension tables.

🎯 Purpose

Built to practice and demonstrate core data engineering skills — ETL design, data modeling, and warehouse architecture.

📜 License

Licensed under the MIT License.

🙋 Author

Yash Kute GitHub · LinkedIn

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Building a modern data warehouse with SQL Server, Including ETL Processes, data modelling and analytics

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