This project demonstrates how to build a modern data warehouse from scratch using SQL Server. The goal was to take messy, disconnected data from two different sources—an ERP system and a CRM system—and transform it into a clean, centralized dataset ready for analysis.

I followed the Medallion Architecture (Bronze, Silver, Gold layers) to ensure the data pipeline is scalable and easier to debug. This project showcases my ability to handle ETL processes, data cleaning, and dimensional modeling.

I designed the data flow in three distinct layers to maintain data quality:

1. The Bronze Layer (Raw Data) This is where the data lands first. I ingested raw CSV files from the source systems directly into SQL Server. At this stage, no changes were made to the data. This creates a historical archive that allows me to re-process data later if business rules change, without needing to go back to the source systems.

2. The Silver Layer (Transformation & Cleaning) This is the "logic" layer. I wrote SQL scripts to clean the raw data.

• Standardization: I renamed columns to follow a consistent naming convention (e.g., ensuring all date columns follow the same format).
• Data Quality: I handled null values, removed duplicates, and fixed data typing issues (like converting text strings to proper Date or Integer types).
• Integration: I merged the data from the ERP and CRM systems so they could "talk" to each other.

3. The Gold Layer (Reporting) This is the final output. I modeled the cleaned data into a Star Schema.

• Fact Tables: Created tables for transactional data (Sales).
• Dimension Tables: Created tables for context (Customers, Products, Calendar). This structure is optimized for performance and is ready to be plugged into visualization tools like Power BI or Tableau.

• Database: Microsoft SQL Server
• Language: T-SQL (Transact-SQL)
• Concepts: ETL, Data Warehousing, Star Schema, Normalization

Building this project helped me solidify several core data engineering concepts:

• Writing complex Stored Procedures to automate the flow of data.
• Understanding the importance of a Date Dimension for time-intelligence analysis.
• Handling data quality issues proactively before they reach the reporting layer.

1. Clone this repository to your local machine.
2. Open the SQL scripts in SSMS (SQL Server Management Studio).
3. Run the setup script to initialize the database.
4. Execute the pipelines in order: Bronze (load), Silver (clean), then Gold (model).

Credit: This project was built following the Data With Baraa SQL Masterclass.

HI!i'm Ajibola Oyefeso an Economics student interested in financial literacy and education.I am a dedicated learner and aspiring Analyst Engineer. I am currently honing my skills in SQL Server and implementing best practices in Data Warehousing.