SQL_ETL_Datawarehouse

🧱 Data Warehouse Project: ETL Pipeline with Bronze, Silver, Gold Layers

📌 Overview

This project showcases a fully developed SQL-based ETL pipeline, structured across Bronze (raw ingestion), Silver (data cleaning), and Gold (business-ready modeling) layers. It is designed to demonstrate scalable data architecture, logical transformations, and analytical precision.

🎯 Objectives

• Ingest and standardize raw CRM and ERP datasets
• Apply cleansing logic to build refined Silver layer tables
• Create star schema models in the Gold layer for KPI tracking and reporting
• Document ETL logic, schemas, and design decisions

🧠 Technologies Used

• SQL Server
• Draw.io (for data model diagrams)
• GitHub (version control)
• VS Code (development environment)

🛠️ Layer Structure

🔹 Bronze Layer

Raw data ingestion, unmodified structure
Example: crm_sales_raw.sql, erp_product_raw.sql

🔸 Silver Layer

Data cleaning, formatting, null handling, filters
Example: clean_crm_sales.sql, clean_customer.sql

🥇 Gold Layer

Dimensional modeling, business logic, aggregated KPIs
Example: dim_customers.sql, fact_sales_summary.sql, reporting views

🧠 DataWarehouseAnalytics Project

A structured analytics pipeline that transforms raw operational data into business insights using layered data engineering and analysis. This project demonstrates SQL modeling, exploratory techniques, and customer/product-level intelligence — perfect for dashboards, reporting, and decision-making.

📁 Folder Structure & Description

Folder Name	Purpose
create_table_bronze/	Raw table creation scripts defining foundational structures
bronze_layer/	Ingestion-level data (minimal transformation) from CRM/ERP systems
silver_layer/	Cleaned and enriched business entities with standard keys and formats
gold_layer/	Dimensional views and fact tables for analytics, dashboard-ready
eda/	Exploratory Data Analysis on product, customer, and transaction data
advance_analysis/	Time-based trends, running totals, moving averages, and change metrics
customer_report/	Customer segmentation and behavioral profiling using lifecycle metrics
product_report/	Product performance, lifecycle analysis, and segmentation

🎯 Key Features

• ✅ End-to-end SQL modeling from raw ingestion to reporting layers
• 📊 Analytical depth: KPIs, segmentation, lifecycle and revenue tiering
• 🧹 Clean naming, zero-division safeguards, intuitive logic
• 🚀 Ready for BI tools: Tableau integration, metric layer compatibility

📈 Use Cases

• Customer behavior analysis & retention strategy
• Product portfolio optimization
• Executive dashboard metrics & performance reporting
• Lifecycle and revenue segmentation

👤 Author

Prem — SQL artisan, data engineering enthusiast, and creative data storyteller.

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