Advanced SQL & Window Functions
Use of analytical SQL patterns: window functions (SUM/AVG OVER, LAG), deterministic window frames, safe division, and robust NULL handling.
Window Functions
LAG
ROWS BETWEEN
Data Warehouse Analytics (SQL Server)
Production-style SQL analytics layer built on the Data Warehouse Gold model (Star Schema): KPI engineering, revenue segmentation, time-series growth analysis, and BI-ready reporting views.
Overview
This project focuses on the analytical layer: from a curated Gold Star Schema, it produces scalable KPI queries, segmentation logic, time-series indicators, and reporting views designed for BI consumption.
Stack: SQL Server, T-SQL, Window Functions, Star Schema, Reporting Views.
Model: Gold Star Schema (facts & dimensions)
Analytics: KPIs • ranking • part-to-whole • time-series • segmentation
Outputs: BI-ready views (customers & products)
What the project demonstrates
Time-Series & Growth Analytics
Monthly aggregation, running totals, moving averages, and MoM/YoY growth computations to analyze trends over time.
Time-Series
MoM / YoY
Running Totals
KPI Engineering & Reporting Views
Business-driven KPIs, customer/product reports, and consolidated views directly consumable by BI tools (e.g., Power BI).
KPI Engineering
Reporting Views
Segmentation
Want to know more?
I can share additional context (data model choices, KPI definitions, performance considerations, limitations, next steps) during a discussion.