Welcome to the MySQL Data Warehouse & Analytics Repository! This project demonstrates how to design and implement a complete data warehouse solution, including data modeling, ETL processes, and SQL-driven analytics to convert raw datasets into valuable business insights.
Goal Build a scalable data warehouse on MySQL that consolidates sales records into a central system for reporting and decision support.
Key Features
- Source Systems: Integrate data from ERP and CRM platforms
- Data Cleaning: Address inconsistencies and ensure data quality before analysis
- Integration: Merge sources into a single analytical model optimized for querying
- Coverage: Use the latest available data only (no historical archiving required)
- Documentation: Provide a clear schema explanation for both analysts and business users
Goal Use SQL queries to extract business intelligence by analyzing:
- Customer behavior patterns
- Product performance metrics
- Sales and revenue trends
These insights equip stakeholders with data-backed KPIs to support better strategies and business growth.
- Database: MySQL, Python
- DataDrawing: Drawio
- Data Integration: Manual ETL workflows (SQL-based transformations)
- Data Sources: ERP and CRM systems
- Modeling: Star-schema style analytical model for queries and reporting
- Reporting: SQL queries for dashboards, KPIs, and insights
This project is distributed under the MIT License. You’re free to use, adapt, and share it with appropriate attribution.
Hi there, I’m David Asikpo — a data analyst passionate about turning complex datasets into actionable insights. My expertise includes SQL, data modeling, and building end-to-end analytics solutions. I’m also expanding my skill set into predictive analysis, machine learning, and data science to solve more advanced challenges.