OPTIMIZING TERADATA, HIVE SQL, AND PYSPARK FOR ENTERPRISE-SCALE FINANCIAL WORKLOADS WITH DISTRIBUTED AND PARALLEL COMPUTING
Keywords:
Distributed Computing, Parallel Processing, Teradata, Hive SQL, PySpark, Financial, Workloads, Big Data Optimization, Enterprise Analytics]Abstract
Financial organizations deal with large amounts of information on transactions, markets and risksthat must be sorted through rapidly and correctly. This research aims to discover how Teradata,Hive SQL and PySpark
References
• Akhund, S. (n.d.). Computing infrastructure and data pipeline for enterprise-scale data preparation.
• Chang, B. R., Tsai, H. F., & Lee, Y. D. (2018). Integrated high-performance platform for fast query response in big data with Hive, Impala, and SparkSQL: A performance evaluation. Applied Sciences, 8(9), 1514. https://doi.org/10.3390/app8091514
Downloads
Published
2022-02-21
How to Cite
Niranjan Reddy Rachamala. (2022). OPTIMIZING TERADATA, HIVE SQL, AND PYSPARK FOR ENTERPRISE-SCALE FINANCIAL WORKLOADS WITH DISTRIBUTED AND PARALLEL COMPUTING . Journal of Computational Analysis and Applications (JoCAAA), 30(2), 730–743. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3441
Issue
Section
Articles
