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A comprehensive guide to building a modern data warehouse with SQL Server, including ETL processes, data modeling, and analytics.

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Data Warehouse and Analytics Project

Welcome to the Data Warehouse and Analytics Project repository! πŸš€
This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.


πŸ—οΈ Data Architecture

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

  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.

πŸ“– Project Overview

This project involves:

  1. Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers.
  2. ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse.
  3. Data Modeling: Developing fact and dimension tables optimized for analytical queries.
  4. Analytics & Reporting: Creating SQL-based reports and dashboards for actionable insights.

🎯 This repository is an excellent resource for professionals and students looking to showcase expertise in:

  • SQL Development
  • Data Architect
  • Data Engineering
  • ETL Pipeline Developer
  • Data Modeling
  • Data Analytics

πŸ› οΈ Important Links & Tools:

Everything is for Free!


πŸš€ 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.

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 docs/requirements.md.

πŸ“‚ Repository Structure

data-warehouse-project/
β”‚
β”œβ”€β”€ datasets/                           # Raw datasets used for the project (ERP and CRM data)
β”‚
β”œβ”€β”€ docs/                               # Project documentation and architecture details
β”‚   β”œβ”€β”€ etl.drawio                      # Draw.io file shows all different techniquies and methods of ETL
β”‚   β”œβ”€β”€ data_architecture.drawio        # Draw.io file shows the project's architecture
β”‚   β”œβ”€β”€ data_catalog.md                 # Catalog of datasets, including field descriptions and metadata
β”‚   β”œβ”€β”€ data_flow.drawio                # Draw.io file for the data flow diagram
β”‚   β”œβ”€β”€ data_models.drawio              # Draw.io file for data models (star schema)
β”‚   β”œβ”€β”€ naming-conventions.md           # Consistent naming guidelines for tables, columns, and files
β”‚
β”œβ”€β”€ scripts/                            # SQL scripts for ETL and transformations
β”‚   β”œβ”€β”€ bronze/                         # Scripts for extracting and loading raw data
β”‚   β”œβ”€β”€ silver/                         # Scripts for cleaning and transforming data
β”‚   β”œβ”€β”€ gold/                           # Scripts for creating analytical models
β”‚
β”œβ”€β”€ tests/                              # Test scripts and quality files
β”‚
β”œβ”€β”€ README.md                           # Project overview and instructions
β”œβ”€β”€ LICENSE                             # License information for the repository
β”œβ”€β”€ .gitignore                          # Files and directories to be ignored by Git
└── requirements.txt                    # Dependencies and requirements for the project

β˜• Stay Connected

Let's stay in touch! Feel free to connect with me on the following platforms:

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All Courses and their materials are completely free, and all I ask is your support through subscribing, liking, and commenting on my channel. Your engagement means the world to me and It help the channel!


πŸ›‘οΈ License

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

🌟 About Me

Hi there! I'm Baraa Khatib Salkini, also known as Data With Baraa. I’m an IT professional and passionate YouTuber on a mission to share knowledge and make working with data enjoyable and engaging!

Let's stay in touch! Feel free to connect with me on the following platforms:

YouTube LinkedIn Website Newsletter PayPal Join