Cover for Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist

Big Data, Data Warehouse and Data Vault

Book2015

Authors:

W.H. Inmon and Daniel Linstedt

Data Architecture: A Primer for the Data Scientist

Big Data, Data Warehouse and Data Vault

Book2015

 

Cover for Data Architecture: A Primer for the Data Scientist

Authors:

W.H. Inmon and Daniel Linstedt

About the book

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Book 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 a ... read full description

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  1. Full text access
  2. Book chapterAbstract only

    1.1 - Corporate Data

    Pages 1-7

  3. Book chapterAbstract only

    1.2 - The Data Infrastructure

    Pages 9-14

  4. Book chapterAbstract only

    1.3 - The “Great Divide”

    Pages 15-20

  5. Book chapterAbstract only

    1.4 - Demographics of Corporate Data

    Pages 21-25

  6. Book chapterAbstract only

    1.5 - Corporate Data Analysis

    Pages 27-31

  7. Book chapterAbstract only

    1.6 - The Life Cycle of Data – Understanding Data Over Time

    Pages 33-37

  8. Book chapterAbstract only

    1.7 - A Brief History of Data

    Pages 39-44

  9. Book chapterAbstract only

    2.1 - A Brief History of Big Data

    Pages 45-48

  10. Book chapterAbstract only

    2.2 - What is Big Data?

    Pages 49-55

  11. Book chapterAbstract only

    2.3 - Parallel Processing

    Pages 57-62

  12. Book chapterAbstract only

    2.4 - Unstructured Data

    Pages 63-70

  13. Book chapterAbstract only

    2.5 - Contextualizing Repetitive Unstructured Data

    Pages 71-72

  14. Book chapterAbstract only

    2.6 - Textual Disambiguation

    Pages 73-81

  15. Book chapterAbstract only

    2.7 - Taxonomies

    Pages 83-90

  16. Book chapterAbstract only

    3.1 - A Brief History of Data Warehouse

    Pages 91-100

  17. Book chapterAbstract only

    3.2 - Integrated Corporate Data

    Pages 101-109

  18. Book chapterAbstract only

    3.3 - Historical Data

    Pages 111-113

  19. Book chapterAbstract only

    3.4 - Data Marts

    Pages 115-119

  20. Book chapterAbstract only

    3.5 - The Operational Data Store

    Pages 121-126

  21. Book chapterAbstract only

    3.6 - What a Data Warehouse is Not

    Pages 127-132

  22. Book chapterAbstract only

    4.1 - Introduction to Data Vault

    Pages 133-137

  23. Book chapterAbstract only

    4.2 - Introduction to Data Vault Modeling

    Pages 139-147

  24. Book chapterAbstract only

    4.3 - Introduction to Data Vault Architecture

    Pages 149-153

  25. Book chapterAbstract only

    4.4 - Introduction to Data Vault Methodology

    Pages 155-162

  26. Book chapterAbstract only

    4.5 - Introduction to Data Vault Implementation

    Pages 163-168

  27. Book chapterAbstract only

    5.1 - The Operational Environment – A Short History

    Pages 169-175

  28. Book chapterAbstract only

    5.2 - The Standard Work Unit

    Pages 177-180

  29. Book chapterAbstract only

    5.3 - Data Modeling for the Structured Environment

    Pages 181-188

  30. Book chapterAbstract only

    5.4 - Metadata

    Pages 189-194

  31. Book chapterAbstract only

    5.5 - Data Governance of Structured Data

    Pages 195-198

  32. Book chapterAbstract only

    6.1 - A Brief History of Data Architecture

    Pages 199-209

  33. Book chapterAbstract only

    6.2 - Big Data/Existing Systems Interface

    Pages 211-218

  34. Book chapterAbstract only

    6.3 - The Data Warehouse/Operational Environment Interface

    Pages 219-224

  35. Book chapterAbstract only

    6.4 - Data Architecture – A High-Level Perspective

    Pages 225-229

  36. Book chapterAbstract only

    7.1 - Repetitive Analytics – Some Basics

    Pages 231-248

  37. Book chapterAbstract only

    7.2 - Analyzing Repetitive Data

    Pages 249-257

  38. Book chapterAbstract only

    7.3 - Repetitive Analysis

    Pages 259-266

  39. Book chapterAbstract only

    8.1 - Nonrepetitive Data

    Pages 267-285

  40. Book chapterAbstract only

    8.2 - Mapping

    Pages 287-289

  41. Book chapterAbstract only

    8.3 - Analytics from Nonrepetitive Data

    Pages 291-304

  42. Book chapterAbstract only

    9.1 - Operational Analytics

    Pages 305-312

  43. Book chapterAbstract only

    10.1 - Operational Analytics

    Pages 313-321

  44. Book chapterAbstract only

    11.1 - Personal Analytics

    Pages 323-328

  45. Book chapterAbstract only

    12.1 - A Composite Data Architecture

    Pages 329-333

  46. Book chapterNo access

    Glossary

    Pages 335-344

  47. Book chapterNo access

    Index

    Pages 345-355

About the book

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

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

Key Features

  • 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
  • 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

Details

ISBN

978-0-12-802044-9

Language

English

Published

2015

Copyright

Copyright © 2015 Elsevier Inc. All rights reserved.

Imprint

Morgan Kaufmann

Authors

W.H. Inmon

Daniel Linstedt