Data Warehouse and Analytics Project

The materials of the data warehouse project: the source files, every script from the videos, and the docs.

You take raw files from two systems, a CRM and an ERP, and build one data warehouse in SQL Server. At the end, the data is clean and ready for analysis.

New here? Open the setup guide in the 00_setup folder. It walks you through installing SQL Server and creating the database, on Windows and Mac, with the fixes for the common errors.

sql-data-warehouse-project/
├── 00_setup/               the setup guide, and the one-click Docker start for Mac
├── 01_datasets/            the six source files the project loads
│   ├── source_crm/         three files from the CRM system
│   └── source_erp/         three files from the ERP system
├── 02_docs/                the requirements, the diagrams and the notes
├── 03_scripts/             the project scripts, in the order of the videos
│   ├── init_database.sql   creates the database: run it first
│   ├── bronze/             loads the raw files
│   ├── silver/             cleans and prepares the data
│   └── gold/               builds the tables for analysis
└── 04_tests/               checks the quality of the silver and gold data

The project reads the six files from C:\sql\dwh_project\01_datasets\. Put the folder there, or change the six paths in 03_scripts/bronze/proc_load_bronze.sql. The videos show the folders without numbers: datasets there is 01_datasets here, scripts is 03_scripts.

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🏗️ Data Architecture

The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:

1. Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
2. Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
3. Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.

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📖 Project Overview

In this project, you:

1. Design the architecture: a modern data warehouse with three layers, bronze, silver and gold.
2. Build the ETL pipelines: load the source files and move the data through the layers.
3. Model the data: facts and dimensions, ready for reports.
4. Analyze it: answer business questions with SQL.

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🛠️ Important Links & Tools

• SQL Server and SSMS: the setup guide in 00_setup has the links and every step.
• Git: a GitHub account, to save and share your project.
• draw.io: to draw the architecture and the data model.
• Notion template: the project plan, ready to copy.
• Notion project steps: every phase and task of the project.

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🚀 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 Sources: Import data from two source systems (ERP and CRM) provided as CSV files.
• Data Quality: Cleanse and resolve data quality issues prior to analysis.
• Integration: Combine both sources into a single, user-friendly data model designed 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.

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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.

For more details, refer to 02_docs/requirements.md.

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🎓 Learn with me

Watch on YouTube, or take the guided version with a certificate on my Academy or Udemy.

Course	YouTube	My Academy	Udemy
SQL	Full course · Playlist	Full Package · Handbook	SQL Bootcamp · SQL for Analytics
Python	Full course · Playlist	Full Package · Handbook	Python Bootcamp
Tableau	Full course · Playlist	Full Package	Tableau Course · Chart Masterclass
Power BI	Full course
Databricks	Full course

Materials on my website: SQL · Python · Tableau · Power BI

The SQL projects

Project	YouTube	Materials	Udemy
Data Warehouse	Watch	this repository	Building a Modern Data Warehouse
Exploratory Data Analysis	Watch	Repository	SQL for Exploratory Data Analysis
Advanced Data Analytics	Watch	same repository

More on YouTube: Data projects · Data careers

🤝 Connect

🛡️ License

MIT. Use it, change it, share it, with attribution.

🌟 About me

Hi, I'm Baraa Khatib Salkini. I spent years in enterprise data engineering and built one of the largest lakehouses at Mercedes-Benz. Today I teach data full-time at Data With Baraa: visually, step by step, from zero to hero.