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An Approach to Solving Current and Future Data Warehousing Big Data Problems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	An Approach to Solving Current and Future Data Warehousing Big Data Problems./
Author:	Dhiman, Shivangi.
Description:	1 online resource (70 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355172911

An Approach to Solving Current and Future Data Warehousing Big Data Problems.
Dhiman, Shivangi.

An Approach to Solving Current and Future Data Warehousing Big Data Problems. - 1 online resource (70 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)

Includes bibliographical references

Data warehouses are an integral component for business decision-making when a large volume of data has to be analyzed. Data in data warehouses traditionally comes from sources including, but not limited to, relational databases, legacy systems, and flat files. The data from these sources has to be processed to conform to the data model and type of the data warehouse. Extract, Transform, and Load (ETL) tools are used to take data from multiple sources to populate a data warehouse, typically into a star schema. After the data is loaded into the star schema, it can be used for query from reporting tools, also known as the reporting layer of the data warehouse.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355172911Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

An Approach to Solving Current and Future Data Warehousing Big Data Problems.
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245 1 3 $a An Approach to Solving Current and Future Data Warehousing Big Data Problems.
264 0 $c 2017
300 $a 1 online resource (70 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Carl Eckberg.
502 $a Thesis (M.S.) $c San Diego State University $d 2017.
504 $a Includes bibliographical references
520 $a Data warehouses are an integral component for business decision-making when a large volume of data has to be analyzed. Data in data warehouses traditionally comes from sources including, but not limited to, relational databases, legacy systems, and flat files. The data from these sources has to be processed to conform to the data model and type of the data warehouse. Extract, Transform, and Load (ETL) tools are used to take data from multiple sources to populate a data warehouse, typically into a star schema. After the data is loaded into the star schema, it can be used for query from reporting tools, also known as the reporting layer of the data warehouse.
520 $a Since the advent of sources like social media, sensor data, phone data and other unstructured sources, the organizations are starting to face the challenge of volume, velocity and variety of data ingesting into their data warehouse. In the past few years the organizations want to get the whole picture of the data in the data warehouse, including all these unstructured data sources that have become a challenge both technologically and financially. According to internet sources the number of tweets sent per day are approximately 500 million, and that gives an idea about how much the volume of data has increased. There is a definite need to solve this Big Data problem in Data warehousing in a scalable, cost-effective way without affecting the end users.
520 $a The Main purpose of this research is to come up with an approach to solve this Data warehousing Big Data problem. This thesis will present a way that can be integrated with both an existing and a new Data warehouse. I will be presenting a hybrid approach to solve this Big Data problem that makes use of the best of both the worlds i.e. Hadoop and Relational databases. I will also present the sample implementation of the Model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a San Diego State University. $b Computer Science. $3 1179359
773 0 $t Masters Abstracts International $g 56-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10621562 $z click for full text (PQDT)