Enterprise Data Warehouse Design Considerations

In this Journal, the base lines for designing an Enterprise Data Warehouse (EDW) to support business analytics in the era of “big data” has been explained. The scope and challenges of building and evolving a very stable and successful EDW architecture to meet new business requirements. These include extreme integration, semi- and un-structured data sources, and petabytes of behavioral data as well as massively parallel relational databases, and then structuring the EDW to support advanced analytics.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-04
DOI
https://doi.org/10.5281/zenodo.13209820
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Enterprise Data Warehouse Design Considerations

Zenodo (CERN European Organization for Nuclear Research)
Big Data and Business Intelligence
article

Enterprise Data Warehouse Design Considerations

article en

Abstract

In this Journal, the base lines for designing an Enterprise Data Warehouse (EDW) to support business analytics in the era of “big data” has been explained. The scope and challenges of building and evolving a very stable and successful EDW architecture to meet new business requirements. These include extreme integration, semi- and un-structured data sources, and petabytes of behavioral data as well as massively parallel relational databases, and then structuring the EDW to support advanced analytics.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 39%
Big Data and Business Intelligence
DOIOpen Access PDFOpenAlex
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Enterprise Data Warehouse Design Considerations · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS