📊 Data Monitoring & SQL Optimization 🚀

🔍 Overview

This project aims to enhance database performance and scalability by implementing structured SQL optimization techniques. It includes index management, query optimization, fragmentation monitoring, and partitioning strategies to enable efficient data retrieval and system performance.

📌 Key Features

• Real-time Data Monitoring 📈
• SQL Query Performance Optimization 🚀
• Index Management & Usage Analysis 🔎
• Handling Missing and Duplicate Indexes 🛠️
• Partitioning for Large Datasets 🔄
• Automatic Statistics Updates 📊

🛠️ Implementation

1️⃣ Statistics Search & Update

• Ensures query execution plans rely on up-to-date statistics for optimal performance.
• Benefit: Reduces full table scans and speeds up queries.

2️⃣ Fragmentation Monitoring & Handling

• Monitors and handles index fragmentation for optimized data retrieval.
• Action:
  • Reorganize for low fragmentation (<30%)
  • Rebuild for high fragmentation (>30%)
• Benefit: Improves read/write performance.

3️⃣ Index Usage Analysis

• Identifies redundant, missing, or unused indexes.
• Benefit: Eliminates unnecessary indexes, reducing maintenance overhead.

4️⃣ Query Performance Optimization

• Detects slow queries and suggests indexing strategies.
• Techniques Used: Execution plans, indexing, join optimization, and scan reduction.
• Benefit: Faster query response times.

5️⃣ Partitioning Setup

• Partitioning strategies for better performance & scalability.
• Includes:
  • Partition Functions
  • Filegroups & Data Files
  • Partition Scheme
  • Partitioned Table Creation
• Benefit: Enables faster data retrieval.

🔧 Tech Stack

• SQL Server / MySQL / PostgreSQL
• Python (for automation scripts)
• Tableau / Power BI (for visualization)
• Linux / Windows Server
• Git for Version Control

📊 Data Visualization (Optional)

• Integrated Tableau / Power BI dashboards for monitoring query performance & database health.