Data architecture : a primer for the data scientist : big data, data warehouse and data vault
Author(s)
Bibliographic Information
Data architecture : a primer for the data scientist : big data, data warehouse and data vault
Morgan Kaufmann, c2015
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Includes index
Description and Table of Contents
Description
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
Table of Contents
1. Corporate Data2. Big Data3. Data Warehouse4. Data Vault5. Operational Systems6. Architecture7. Analysis and Visualization of Data8. Analytics for Structured Data9. Analytics for Unstructured Repetitive Data10. Analytics for Unstructured Non-Repetitive Data11. Glossary of Terms
by "Nielsen BookData"