📊 Enterprise Data Warehouse & Analytics Project

Welcome to the Data Warehouse & Analytics Project!
This project is a practical, end-to-end solution that demonstrates how to build a modern data warehouse using SQL Server, following best practices in data engineering, ETL, and analytics.

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🏗️ Architecture: Medallion Layered Design

I’ve structured the warehouse using the Medallion Architecture—a layered approach that brings clarity and scalability.

🔹 Bronze Layer – Raw Ingestion

• Raw data is ingested directly from CRM and ERP source files (CSV format)
• Stored as-is in PostgreSQL tables for traceability and audit

🔸 Silver Layer – Cleaned & Modeled

• Data is cleansed, standardized, and normalized
• Transformations handle missing values, formatting, and structural consistency
• Designed for joining and enrichment across data domains

🟡 Gold Layer – Business-Ready

• Star schema with well-defined fact and dimension tables
• Optimized for analytical queries and BI consumption
• Enables key insights into customer behavior, product sales, and operational metrics

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🚀 Project Requirements

🏗️ Data Engineering – Building the Data Warehouse

🎯 Objective

Developing a modern data warehouse using SQL Server to consolidate sales-related data from ERP and CRM systems, enabling efficient analytical reporting and data-driven decision-making.

📋 Specifications

• Data Sources:
  Import data from two distinct source systems – CRM and ERP – provided as CSV files.

• Data Quality:
  Cleanse, standardize, and resolve inconsistencies in the raw data before any transformation or analysis.

• Integration:
  Combine both data sources into a unified, user-friendly data model optimized for analytical queries.

• Data Scope:
  Focus on the most recent dataset only. Historical tracking (historization) is not within the scope of this project.

• Documentation:
  Provide clear and concise documentation of the data model, table relationships, and schema structure to support both business stakeholders and data analysts.

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📊 BI – Analytics & Reporting

🎯 Objective

Developing SQL-based analytics and reporting logic to deliver actionable insights that support strategic business decisions.

🔍 Focus Areas

• Customer Behavior
  Understand purchase patterns, segmentation, and engagement levels.

• Product Performance
  Identify best-selling products, underperformers, and product mix trends.

• Sales Trends
  Analyze sales over time, across locations, and customer types to uncover growth opportunities.

These insights are designed to empower stakeholders with key business metrics and help steer decisions with confidence.

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🔗 Notion Project Link: SQL Data Warehouse Project

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