SQL OPTIMIZATION TECHNIQUES FOR REAL-TIME ANALYTICS A COMPREHENSIVE REVIEW WITH CASE STUDY

Abstract

This paper presents SQL Optimization Techniques for Real-Time Analytics A Comprehensive Review with Case Study of real-time big data . As data becomes the backbone of organizational strategies, traditional SQL systems face significant challenges when handling high-velocity, high-volume workloads characteristic of modern applications such as IoT ecosystems, financial trading, e-commerce platforms, and healthcare systems. This review systematically examines optimization techniques including query optimization strategies, advanced indexing methods, parallel and distributed SQL processing, resource optimization approaches, and hybrid NewSQL solutions. Through analysis of recent literature spanning 2022-2024, we identify key developments in adaptive query processing, automatic indexing, machine learning-driven optimizers, and cloud-native database solutions. A practical case study comparing MySQL/MariaDB and PostgreSQL 16 with large-scale datasets demonstrates that proper indexing strategies can reduce query latency by up to 50%, while modern query structures and performance monitoring ensure system scalability. The findings reveal that even with considerable strides forward in SQL optimization, problems persist in balancing scalability, cost-efficiency, and fault tolerance, particularly in heterogeneous cloud environments. This review contributes to understanding current optimization paradigms and identifies future research directions toward intelligent, adaptive SQL systems capable of meeting the demands of real-time analytics workloads.