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ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS

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Bibliographic Details
Title: ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS
Authors: Rahman, Md Mostafizur, Islam, Siful, Kamruzzaman, Md, Joy, Zihad Hasan
Source: Academic Journal on Business Administration, Innovation & Sustainability; Vol. 4 No. 3 (2024): Academic Journal on Business Administration, Innovation & Sustainability; 1-14 ; 2997-9552
Publisher Information: All Academic Research
Publication Year: 2024
Subject Terms: SQL Query Optimization, Multi-level Indexing, Query Rewriting, Dynamic Query Execution Plans, Big Data Analytics
Description: This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.
Document Type: article in journal/newspaper
File Description: application/pdf
Language: English
Relation: https://allacademicresearch.com/index.php/AJBAIS/article/view/77/72; https://allacademicresearch.com/index.php/AJBAIS/article/view/77
Availability: https://allacademicresearch.com/index.php/AJBAIS/article/view/77
Rights: Copyright (c) 2024 Academic Journal on Business Administration, Innovation & Sustainability
Accession Number: edsbas.CDEBAFD5
Database: BASE
Description
Abstract:This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.