Data Architecture: A Primer for the Data Scientist

Big Data, Data Warehouse and Data Vault

Nonfiction, Computers, Database Management, Application Software, Business Software, General Computing
Cover of the book Data Architecture: A Primer for the Data Scientist by W.H. Inmon, Daniel Linstedt, Elsevier Science
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: W.H. Inmon, Daniel Linstedt ISBN: 9780128020913
Publisher: Elsevier Science Publication: November 26, 2014
Imprint: Morgan Kaufmann Language: English
Author: W.H. Inmon, Daniel Linstedt
ISBN: 9780128020913
Publisher: Elsevier Science
Publication: November 26, 2014
Imprint: Morgan Kaufmann
Language: English

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

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

More books from Elsevier Science

Cover of the book 22nd European Symposium on Computer Aided Process Engineering by W.H. Inmon, Daniel Linstedt
Cover of the book Practical Predictive Analytics and Decisioning Systems for Medicine by W.H. Inmon, Daniel Linstedt
Cover of the book Advances in Organometallic Chemistry by W.H. Inmon, Daniel Linstedt
Cover of the book Applied Time Series Analysis by W.H. Inmon, Daniel Linstedt
Cover of the book Quantitative Methods in Reservoir Engineering by W.H. Inmon, Daniel Linstedt
Cover of the book Advances in Applied Microbiology by W.H. Inmon, Daniel Linstedt
Cover of the book Dielectric Materials for Wireless Communication by W.H. Inmon, Daniel Linstedt
Cover of the book Atlas of Microbial Mat Features Preserved within the Siliciclastic Rock Record by W.H. Inmon, Daniel Linstedt
Cover of the book Handbook of Key Global Financial Markets, Institutions, and Infrastructure by W.H. Inmon, Daniel Linstedt
Cover of the book Introduction to Ecological Biochemistry by W.H. Inmon, Daniel Linstedt
Cover of the book Behavioral Addictions by W.H. Inmon, Daniel Linstedt
Cover of the book Platform Ecosystems by W.H. Inmon, Daniel Linstedt
Cover of the book Advances in Food and Nutrition Research by W.H. Inmon, Daniel Linstedt
Cover of the book Nanotechnology Environmental Health and Safety by W.H. Inmon, Daniel Linstedt
Cover of the book Bio-Geotechnologies for Mine Site Rehabilitation by W.H. Inmon, Daniel Linstedt
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy