Data Warehouse and Analytics Project (ETL)

Welcome to the Data Warehouse and Analytics Project repository! 🚀
This project demonstrates a comprehensive data warehousing and analytics solution, from building a data warehouse to generating actionable insights.

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📖 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

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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.

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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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🛡️ 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 abdelmalik9, also known as abd el malik.