Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/45327

TitleModelling and implementing big data warehouses for decision support
Author(s)Santos, Maribel Yasmina
Martinho, Bruno
Costa, Carlos
KeywordsBig data
Data model
Data warehouse
Hive
NoSQL
Issue dateMar-2017
PublisherTaylor & Francis
JournalJournal of Management Analytics
CitationSantos, Maribel Yasmina, Bruno Martinho and Carlos Costa, "Modelling and Implementing Big Data Warehouses for Decision Support", Journal of Management Analytics, 4 (2), 1-19, ISSN: 2327-0039, DOI: 10.1080/23270012.2017.1304292, March, 2017, Taylor & Francis.
Abstract(s)In the era of Big Data, many NoSQL databases emerged for the storage and later processing of vast volumes of data, using data structures that can follow columnar, key-value, document or graph formats. For analytical contexts, requiring a Big Data Warehouse, Hive is used as the driving force, allowing the analysis of vast amounts of data. Data models in Hive are usually defined taking into consideration the queries that need to be answered. In this work, a set of rules is presented for the transformation of multidimensional data models into Hive tables, making available data at different levels of detail. These several levels are suited for answering different queries, depending on the analytical needs. After the identification of the Hive tables, this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.
TypeArticle
URIhttp://hdl.handle.net/1822/45327
DOI10.1080/23270012.2017.1304292
ISSN2327-0039
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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