TRENDS IN DATA WAREHOUSING TECHNIQUES

Naveen Kunnathuvalappil Hariharan

No 6cyq4, OSF Preprints from Center for Open Science

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.

Date: 2019-08-12
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6138dc1d28b37600a77d3989/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:6cyq4

DOI: 10.31219/osf.io/6cyq4

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().