OPTIMIZING QUERY PERFORMANCE IN CLOUD DATA WAREHOUSES: A FRAMEWORK FOR IDENTIFYING AND ADDRESSING PERFORMANCE BOTTLENECKS
Keywords:
Data Partitioning, Indexing Strategies, Query Optimization Algorithms, Performance Monitoring, Resource UtilizationAbstract
Cloud data warehouses have become increasingly popular due to their scalability, flexibility, and cost-effectiveness. However, optimizing query performance in such environments can be challenging. This research paper presents a framework for identifying performance bottlenecks and implementing targeted optimizations in cloud data warehouses. The proposed framework considers various factors that influence query execution time, including data partitioning, indexing strategies, and query optimization algorithms. A case study is conducted to demonstrate the effectiveness of the framework in improving query performance in a real-world cloud data warehouse environment. According to the findings, applying the suggested optimizations can cut query execution time by up to 45% while also improving resource utilization by 30%. The findings of this research can help organizations enhance the performance of their cloud data warehouses and make informed decisions regarding optimization strategies.
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Copyright (c) 2024 Sadha Shiva Reddy Chilukoori , Shashikanth Gangarapu , Chaitanya Kumar Kadiyala (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
