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Trends In Data Warehousing Techniques

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  • Hariharan, Naveen Kunnathuvalappil

Abstract

Financial data volumes are increasing, and this appears to be a long-term trend, implying that data management development will be crucial over the next few decades. Because financial data is sometimes real-time data, it is constantly generated, resulting in a massive amount of financial data produced in a short period of time. The volume, diversity, and velocity of Big Financial Data are highlighting the significant limitations of traditional Data Warehouses (DWs). Their rigid relational model, high scalability costs, and sometimes inefficient performance pave the way for new methods and technologies. The majority of the technologies used in background processing and storage research were previously the subject of research in their early stages. The Apache Foundation and Google are the two most important initiatives. For dealing with large financial data, three techniques outperform relational databases and traditional ETL processing: NoSQL and NewSQL storage, and MapReduce processing.

Suggested Citation

  • Hariharan, Naveen Kunnathuvalappil, 2019. "Trends In Data Warehousing Techniques," OSF Preprints 6cyq4, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:6cyq4
    DOI: 10.31219/osf.io/6cyq4
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    References listed on IDEAS

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    1. Mahda Garmaki & Imed Boughzala & Samuel Fosso Wamba, 2016. "The effect of big data analytics capability on firm performance," Grenoble Ecole de Management (Post-Print) hal-01414811, HAL.
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