Building a modern data warehouse with SQL server, including ETL process, data modeling and analytics.
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.
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.
This 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
Everything is for Free!
- Datasets: Access to the project dataset (csv files).
- SQL Server Express: Lightweight server for hosting your SQL database.
- SQL Server Management Studio (SSMS): GUI for managing and interacting with databases.
- Git Repository: Set up a GitHub account and repository to manage, version, and collaborate on your code efficiently.
- DrawIO: Design data architecture, models, flows, and diagrams.
- Notion: Get the Project Template from Notion
- Notion Project Steps: Access to All Project Phases and Tasks.
Develop a modern data warehouse using SQL Server to consolidate sales data, enabling analytical reporting and informed decision-making.
- 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.
Design and develop SQL-driven analytical dashboards that transform raw data into clear, actionable business insights across key performance areas:
- 🧍 Customer Behavior
- 📦 Product Performance
- 📈 Sales Trends
These insights support data-driven decision-making, helping stakeholders identify patterns, monitor performance, and uncover growth opportunities.
Focus Areas:
- Customer segmentation & engagement
- Purchasing patterns
- Retention and churn indicators
Focus Areas:
- Top & underperforming products
- Revenue contribution by product
- Product-level KPIs
Focus Areas:
- Time-based sales trends
- Seasonal patterns
- Growth and decline indicators
