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This project demonstrates how SQL alone can be used as a complete data analytics and business intelligence engine — without relying on external BI tools.
Each SQL script in this repository solves a specific real-world business problem, ranging from exploratory analysis to advanced performance and segmentation reporting.
It utilizes a star-schema warehouse with three tables: fact_sales, dim_customers, and dim_products.
- Apply advanced SQL concepts (CTEs, window functions, subqueries, joins) to analyze large datasets.
- Simulate a real business intelligence workflow — from data exploration to executive insights.
- Demonstrate how to translate business questions into analytical SQL logic.
- Common Table Expressions (CTEs) for query modularity and readability
- Window Functions for ranking, cumulative sums, and moving averages
- Joins & Subqueries for multi-table integration
- Aggregation & Grouping for business KPI generation
- Parameterized Filters for flexible scenario analysis
- Which products and categories drive the most revenue?
- How does customer retention vary by segment?
- What are the month-over-month and year-over-year growth trends?
- Which regions or products underperform compared to averages?
- How can data segmentation improve decision-making?
- Developed a SQL-driven data analytics roadmap mirroring real BI systems.
- Strengthened ability to translate