📊 Structured Query Language (SQL) Project

This repository contains a comprehensive collection of SQL queries, concepts, and practical implementations developed to strengthen database querying and data analysis skills. The project focuses on real-world business scenarios, including sales analysis, forecasting, error metrics, joins, window functions, and performance optimization.

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🚀 Project Overview

The primary goal of this project is to demonstrate:

• Strong understanding of SQL fundamentals and advanced concepts
• Ability to work with large datasets
• Practical use of SQL in data analysis and reporting
• Query optimization using indexes and window functions

The database used in this project is sourced from a realistic business dataset and is suitable for analytics-based use cases.

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🗂️ Database Description

The project uses multiple fact and dimension tables, including but not limited to:

• fact_sales_monthly
• fact_forecast_monthly
• fact_act_est
• dim_product
• dim_customer

These tables simulate real-world business operations such as sales transactions, demand forecasting, and customer-product relationships.

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🛠️ SQL Concepts Covered

• ✅ Basic SQL Queries (SELECT, WHERE, ORDER BY)
• ✅ Aggregate Functions (SUM, COUNT, AVG, ABS)
• ✅ Joins
  • INNER JOIN
  • LEFT JOIN
  • RIGHT JOIN
  • FULL OUTER JOIN (using UNION)
• ✅ Subqueries & Common Table Expressions (CTEs)
• ✅ Window Functions
  • OVER()
  • PARTITION BY
  • DENSE_RANK()
• ✅ Grouping & Aggregation
• ✅ Error Metrics Calculation
  • Net Error
  • Absolute Error
  • Percentage Error
• ✅ Indexing & Performance Optimization
• ✅ Query Debugging and Optimization

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📈 Sample Use Cases Implemented

• Top N products by division using window functions
• Sales vs Forecast comparison analysis
• Forecast accuracy and error percentage calculation
• Customer and product-level performance analysis
• Handling large datasets efficiently using indexes

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⚙️ Tools & Technologies

• Database: MySQL
• Language: SQL
• Environment: MySQL Workbench
• Version Control: Git & GitHub

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📌 How to Use This Repository

1. Clone the repository:

   git clone https://github.com/Manojsv20/Structured_query_language.git

Import the database into MySQL

Execute the SQL scripts provided

Analyze and modify queries as required

🎯 Learning Outcomes Gained hands-on experience with complex SQL queries

Improved understanding of data analysis using SQL

Learned query optimization techniques

Applied SQL concepts to real-world datasets

👤 Author Manoj S V B.E – Signal Processing Velammal College of Engineering and Technology

📄 License This project is intended for learning and educational purposes.

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