Data Architecture A Primer for the Data Scientist

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

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  • Author : W.H. Inmon
  • Publisher : Morgan Kaufmann
  • Pages : 378 pages
  • ISBN : 0128020911
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKData Architecture A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
  • Author : W.H. Inmon,Daniel Linstedt
  • Publisher : Morgan Kaufmann
  • Release : 26 November 2014
GET THIS BOOKData Architecture: A Primer for the Data Scientist

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references

Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
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  • Publisher : Academic Press
  • Release : 30 April 2019
GET THIS BOOKData Architecture: A Primer for the Data Scientist

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second

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  • Publisher : "O'Reilly Media, Inc."
  • Release : 27 July 2013
GET THIS BOOKData Science for Business

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  • Publisher : Academic Press
  • Release : 10 May 2017
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  • Publisher : "O'Reilly Media, Inc."
  • Release : 10 May 2017
GET THIS BOOKPractical Statistics for Data Scientists

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  • Publisher : Apress
  • Release : 31 December 2015
GET THIS BOOKScalable Big Data Architecture

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  • Publisher : Morgan Kaufmann
  • Release : 15 September 2015
GET THIS BOOKBuilding a Scalable Data Warehouse with Data Vault 2.0

The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of

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  • Publisher : "O'Reilly Media, Inc."
  • Release : 21 February 2012
GET THIS BOOKData for the Public Good

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  • Publisher : Elsevier
  • Release : 09 May 2011
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  • Publisher : "O'Reilly Media, Inc."
  • Release : 21 February 2019
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  • Publisher : "O'Reilly Media, Inc."
  • Release : 05 December 2018
GET THIS BOOKArchitecting Modern Data Platforms

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  • Release : 04 March 2021
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  • Publisher : John Wiley & Sons
  • Release : 11 December 2015
GET THIS BOOKBig Data MBA

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