Data Partitioning Strategies for Optimized Query Performance in Cloud BI Tools
Author(s): Santosh Vinnakota
Publication #: 2503031
Date of Publication: 07.08.2019
Country: USA
Pages: 1-12
Published In: Volume 5 Issue 4 August-2019
DOI: https://doi.org/10.5281/zenodo.15026925
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
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