SQL DATA ANALYSIS FOR BIG DATA OPTIMIZATION: BEST PRACTICES AND PITFALLS

Abstract

The theses investigates SQL optimization techniques essential for enhancing query performance, reducing resource consumption, and ensuring efficient data retrieval in large-scale database systems. It outlines seven key strategies: avoiding functions on WHERE columns to leverage indexes, partitioning large tables for faster searches, filtering data early to minimize processing, utilizing indexes for frequent queries, applying TOP (LIMIT) for testing, optimizing ON clauses in joins, and avoiding unnecessary DISTINCT operations. These methods enhance query execution speed, scalability, and system performance in big data environments. Practical examples and common pitfalls are highlighted, providing actionable insights for database administrators and developers to build responsive, resource-efficient applications while managing complex queries and large datasets effectively.