https://github.com/vishal-bhandary/sql-data-analytics
This repository contains a collection of SQL scripts demonstrating various analytical techniques, such as changes over time, cumulative, performance, data segmentation, part-to-whole analysis.
https://github.com/vishal-bhandary/sql-data-analytics
analytics business-intelligence customer-segmentation dashboarding data-analysis data-reporting data-visualization data-warehouse etl kpi product-analysis sql sql-server star-schema t-sql
Last synced: 2 months ago
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This repository contains a collection of SQL scripts demonstrating various analytical techniques, such as changes over time, cumulative, performance, data segmentation, part-to-whole analysis.
- Host: GitHub
- URL: https://github.com/vishal-bhandary/sql-data-analytics
- Owner: Vishal-bhandary
- License: mit
- Created: 2025-07-17T14:24:41.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-17T14:43:08.000Z (3 months ago)
- Last Synced: 2025-07-17T16:40:59.983Z (3 months ago)
- Topics: analytics, business-intelligence, customer-segmentation, dashboarding, data-analysis, data-reporting, data-visualization, data-warehouse, etl, kpi, product-analysis, sql, sql-server, star-schema, t-sql
- Language: TSQL
- Homepage:
- Size: 5.89 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📊 SQL Data Analytics Project
A hands-on, analytics-driven SQL project built to uncover insights from a modern data warehouse. This project uses advanced T-SQL queries to perform data exploration, segmentation, trend analysis, and KPI generation using dimensional modeling (star schema). It's ideal for showcasing data storytelling and analytical problem-solving skills.
---
## 📚 Table of Contents
* [🔍 Project Overview](#-project-overview)
* [🎯 Objectives](#-objectives)
* [🧱 Data Structure](#-data-structure)
* [📊 Analysis Modules](#-analysis-modules)
* [📈 Sample Insights](#-sample-insights)
* [🚀 How to Run](#-how-to-run)
* [🧰 Tech Stack](#-tech-stack)
* [🔮 Future Enhancements](#-future-enhancements)
* [📄 License](#-license)
---
## 🔍 Project Overview
This project simulates real-world analytics tasks performed over a cleaned and modeled SQL data warehouse. By leveraging structured queries, it enables business users and analysts to perform:
* Customer behavior analysis
* Product performance reviews
* Segmentation & cohort analysis
* Revenue and trend forecasting
* KPI dashboard reporting
The analysis is run on a **star schema** composed of dimension and fact tables in the `gold` layer.
---
## 🎯 Objectives
* Explore and validate database structures and metadata.
* Generate meaningful business metrics (sales, orders, customers).
* Perform segment-wise, time-series, and trend analyses.
* Build reusable analytical views and reports.
* Leverage SQL window functions for ranking and performance tracking.
* Enable customer and product segmentation.
---
## 🧱 Data Structure
**Star Schema Overview (Gold Layer):**
* `dim_customers` – Enriched customer demographics and CRM data
* `dim_products` – Product attributes, categories, cost, and life-cycle
* `fact_sales` – All transactional order data (sales, quantity, pricing)
Additional analytical views:
* `report_customers` – Customer-focused KPI summary and segmentation
* `report_products` – Product performance, ranking, and revenue classification
---
## 📊 Analysis Modules
The project contains well-documented scripts categorized as follows:
| 🔹 Module | 🧠 Purpose |
| -------------------------- | --------------------------------------------------------------------------- |
| **Database Exploration** | Understand schema and metadata using `INFORMATION_SCHEMA` |
| **Dimensions Exploration** | Extract unique values (e.g., countries, categories) for analysis |
| **Date Range Analysis** | Understand data availability over time using `MIN()`, `MAX()`, `DATEDIFF()` |
| **Key Metrics** | Calculate totals, averages, and counts (e.g., sales, orders, products) |
| **Magnitude Analysis** | Analyze grouped metrics by category, gender, region, etc. |
| **Ranking Analysis** | Use `TOP`, `RANK()`, `ROW_NUMBER()` to rank top/bottom performers |
| **Cumulative Analysis** | Track running totals and moving averages |
| **Performance Trends** | Perform YoY, MoM, and average deviation analysis using `LAG()` |
| **Segmentation Analysis** | Classify customers/products based on business logic |
| **Part-to-Whole Analysis** | Analyze category contribution to total revenue |
| **Customer Report View** | Full customer profiling: orders, lifetime, recency, segmentation |
| **Product Report View** | Full product performance: quantity, customers, segment, revenue metrics |
---
## 📈 Sample Insights
> 📌 A few example outputs this project can generate:
* "Top 10 customers generated 40% of revenue"
* "Products costing over \$1,000 contribute 60% of total sales"
* "Category ‘Mountain Bikes’ has the highest average monthly revenue"
* "Customer churn can be identified by tracking recency vs lifespan"
* "Segmented customers into `VIP`, `Regular`, and `New` based on behavior"
---
## 🚀 How to Run
1. Ensure the Gold Layer tables (`dim_customers`, `dim_products`, `fact_sales`) are available and populated.
2. Run each script independently or as part of an orchestrated SQL job in SSMS.
3. You may also convert key queries into **SQL views** for reporting or dashboarding purposes.
4. Use tools like **Power BI**, **Tableau**, or **Excel** to connect to these views for visualization.
---
## 🧰 Tech Stack
* **SQL Server / T-SQL**
* **Dimensional Modeling (Star Schema)**
* **Advanced SQL Functions**
* Window Functions (`RANK()`, `LAG()`, `ROW_NUMBER()`)
* Aggregations (`SUM()`, `AVG()`, `COUNT()`)
* String, Date, and CASE logic
---
## 🔮 Future Enhancements
* Add parameterized stored procedures for dynamic filtering
* Build scheduled dashboards using Power BI
* Include predictive logic using SQL ML Services
* Introduce real-time metrics using SQL Change Data Capture (CDC)
---
## 📄 License
This project is released under the [MIT License](LICENSE).