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. Designed as a portfolio project, it highlights industry best practices in data engineering and analytics.
🏗️ Data Architecture:
The data architecture for this project follows Medallion Architecture Bronze, Silver, and Gold layers:
- Bronze Layer: Stores raw data as-is from the source systems. Data is ingested from CSV Files into SQL Server Database.
- Silver Layer: This layer includes data cleansing, standardization, and normalization processes to prepare data for analysis.
- Gold Layer: Houses business-ready data modeled into a star schema required for reporting and analytics.
📖 Project Overview:
his project involves:
Data Architecture: Designing a Modern Data Warehouse Using Medallion Architecture Bronze, Silver, and Gold layers. ETL Pipelines: Extracting, transforming, and loading data from source systems into the warehouse. Data Modeling: Developing fact and dimension tables optimized for analytical queries. 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
🚀 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.