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Data Partitioning Strategies for Optimized Query Performance in Cloud BI Tools
Authors
Santosh Vinnakota
Abstract
Efficient data partitioning is crucial for optimizing query performance in cloud-based Business Intelligence (BI) tools. With increasing data volumes, traditional query processing approaches become inefficient, leading to high query latency and performance bottlenecks. This paper explores various data partitioning strategies, including horizontal, vertical, range, hash, and hybrid partitioning, to enhance query performance. Additionally, we analyze the impact of partitioning on distributed query execution, indexing, and caching mechanisms in cloud BI environments. We demonstrate the effectiveness of partitioning techniques in reducing query execution time and improving scalability.
Keywords
Data Partitioning, Cloud BI, Query Performance, Distributed Databases, Optimization
Citation
Data Partitioning Strategies for Optimized Query Performance in Cloud BI Tools. Santosh Vinnakota. 2019. IJIRCT, Volume 5, Issue 4. Pages 1-12. https://www.ijirct.org/viewPaper.php?paperId=2503031