Welcome to my SQL portfolio! This repository showcases hands-on SQL projects focused on data testing, analysis, and complex query building using real-world business scenarios. The goal of each project is to demonstrate advanced SQL skills, logical thinking, and practical data insights, with clear documentation and output examples.
Analyse sales data using complex SQL techniques like window functions, aggregations, and ranking to generate business insights.
- 🔸 SQL File:
Project-1/Sales_Data_Analysis_Using_Advanced_SQL.sql - 📄 Description: Sales patterns, top-performing regions, monthly trends
- 📑 Output: PDF report with insights and SQL results
Practice and solve a wide range of SQL challenges using subqueries, APPLY operators, and nested logic.
- 🔸 SQL File:
Project-2/Subqueries_and_APPLY_SQL_Exercises.sql - 📄 Description: Subquery-based use cases including correlated queries and APPLY logic
- 📑 Output: Sample outputs included in PDF format
Extract insights from transportation data using ranking, partitioning, and cumulative metrics with advanced window functions.
- 🔸 SQL File:
Project-3/Travel_Data_Insights_SQL_Window_Functions1.sql - 📄 Description: Analyse routes by speed, rank, and cumulative stats
- 📑 Output: Window function use-case results in PDF format
Extract insights from transportation data using ranking, partitioning, and cumulative metrics with advanced window functions.
- 🔸 SQL File:
Project-4/Travel_Route_Insights_SQL_Window_Functions2.sql - 📄 Description: Analyse routes by speed, rank, and cumulative stats
- 📑 Output: Window function use-case results in PDF format
Simulate HR database queries using various JOIN techniques to extract employee performance, department data, and salary trends.
- 🔸 SQL File:
Project-5/HR_Projects_Join_Based_SQL_Exercises.sql - 📄 Description: Practical JOIN use-cases for HR systems
- 📑 Output: Structured JOIN-based results
Explore recursive SQL with hierarchical route data and build route chaining, stop-tracing, and level-wise path breakdowns.
- 🔸 SQL File:
Project-6/Travel_Route_Analysis_Using_Recursive_SQL.sql - 📄 Description: Recursive CTEs for travel route insights
- 📑 Output: Hierarchical and path-based results using recursion
Practice advanced data grouping, subtotals, and dynamic aggregations using ROLLUP, CUBE, and GROUPING SETS.
- 🔸 SQL File:
Project-7/Sales_Data_Aggregation_and_Grouping_SQL_Exercises.sql - 📄 Description: Dynamic subtotals, category-wise grouping, and per cent-of-total logic
- 📑 Output: Business summary reporting in SQL
Use SQL CASE logic to assign loyalty labels, calculate bonuses, compare actuals vs targets, and apply business rules.
- 🔸 SQL File:
Project-8/Sales_Data_Case_Statement_SQL_Exercises.sql - 📄 Description: Business classification using CASE, label generation, performance comparison
- 📑 Output: Label-based and bonus-driven outputs in PDF
Model employee hierarchies, department performance, and workforce analytics using recursive and modular CTEs.
- 🔸 SQL File:
Project-9/Employee_Analytics_Using_CTEs_and_Recursion_SQL.sql - 📄 Description: Recursion, grades, reporting chains, birthday alerts, and team size tracking
- 📑 Output: HR insights using multi-level CTEs
Solve 24 real-world SQL challenges related to KPIs, diversity metrics, managerial grading, and org structure.
- 🔸 SQL File:
Project-10/Advanced_RealWorld_SQL_Challenges_With_CTEs.sql - 📄 Description: Recursive logic, composite metrics, reporting trees, underutilization tracking
- 📑 Output: Real-world HR & performance dashboard logic
Use UNION, INTERSECT, and EXCEPT to compare historical trends, handoffs, dropped categories, and product overlap.
- 🔸 SQL File:
Project-11/Sales_Analytics_Using_SQL_Set_Operators.sql - 📄 Description: Set logic applied to dropped sales, missing targets, and cross-region overlaps
- 📑 Output: Historical vs actual logic through set operations
Perform practical data formatting, currency cleansing, phone normalisation, CSV splitting, and report-ready string design.
- 🔸 SQL File:
Project-12/Data_Transformation_And_Cleaning_SQL_Exercises.sql - 📄 Description: Real-world data cleanup, export formatting, loyalty tiers, tag parsing
- 📑 Output: Dashboard-ready formatted outputs
Leverage advanced window functions to normalise scores, calculate rolling averages, and assign loyalty labels.
- 🔸 SQL File:
Project-13/Advanced_Analytics_With_SQL_Window_Functions.sql - 📄 Description: Z-scores, contribution %, top N logic, and conditional window ranking
- 📑 Output: Scorecards and trend-smoothing in SQL
Automate reporting, segmentation, and reusable lookups using stored procedures, scalar functions, and TVFs.
- 🔸 SQL File:
Project-14/Stored_Procedures_and_Functions_For_Business_Reporting.sql - 📄 Description: Encapsulated business logic with TRY...CATCH, output params, and function reuse
- 📑 Output: Error-safe procedures and function-driven outputs
Vijay Geete
9+ years of experience in QA Engineering, Application Management, Data Testing and Support in the Banking and FinTech domain. Passionate about clean SQL logic, test-driven data quality, and turning raw data into actionable insights.
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💼 Upwork Profile https://www.upwork.com/freelancers/~0133cf7ab6470793d7?mp_source=share
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📧 Email: vijaygokul13@outlook.com
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🌐 LinkedIn: www.linkedin.com/in/vijay-geete-21a39370
Each project contains:
SQLscript fileProject Description(txt)Project Description(pdf)Project Description(docx)
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