Advanced SQL Projects.

This repository contains a collection of advanced SQL projects focused on real-world industrial and financial data scenarios. These projects demonstrate expertise in complex query optimization, schema design, and time-series data analysis using PostgreSQL.

🚀 Featured Projects

1.Zepto Data Analysis

🚀 Overview

This project is a hands-on SQL practice project built around a dataset (zepto_sales.csv ).
The goal of this project is to strengthen SQL fundamentals by working with real-world-like data, writing queries, and extracting meaningful insights.

Instead of just learning syntax, this project focuses on thinking in SQL — how to ask the right questions from data.

2.Retail Sales Analysis

Level: Beginner
Database: p1_retail_db

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.

3.TechStore Sales & Inventory – SQL Project

A beginner‑to‑intermediate SQL project that demonstrates database design, data insertion, and complex querying on a tech‑store sales system.
Perfect for showcasing SQL skills on GitHub and in your portfolio.

📌 Project Overview

This project simulates an online tech store that sells laptops, smartphones, and accessories.
You can:

• Design and create a normalized database schema
• Insert realistic sample data
• Run a variety of SQL queries (joins, aggregations, subqueries, CTEs, etc.)

This is a great way to demonstrate intermediate SQL skills to recruiters and hiring managers.

🛠️ Technical Stack

• Database: PostgreSQL
• Key Techniques: Window Functions, Recursive CTEs, Subqueries, Range Partitioning, Row-level Locking, Performance Tuning.

4. FinTrack is a high-performance SQL analytics project

designed to simulate a real-world FinTech environment. The project transforms raw transactional data into actionable business intelligence using advanced PostgreSQL techniques.

🛠️ Tech Stack

Database: PostgreSQL Core Competencies: Window Functions, CTEs, Data Normalization, Time-Series Analysis.

📈 Key Learning Outcomes

• Mastery of PostgreSQL window functions for trend analysis.
• Ability to design scalable database schemas for IoT and time-series applications.
• Optimizing complex queries to handle large datasets efficiently.

📫 Contact

Mayank Gariya

• Email: mayankgariya482@gmail.com
• GitHub: mayank-gariya