Cover for Data Architecture

Data Architecture

A Primer for the Data Scientist

Book • Second Edition2019

Authors:

W.H. Inmon, Daniel Linstedt and Mary Levins

Data Architecture

A Primer for the Data Scientist

Book • Second Edition2019

 

Cover for Data Architecture

Authors:

W.H. Inmon, Daniel Linstedt and Mary Levins

About the book

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Book description

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 ... read full description

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  2. Book chapterAbstract only

    Chapter 1.1 - An Introduction to Data Architecture

    Pages 1-5

  3. Book chapterAbstract only

    Chapter 1.2 - The Data Infrastructure

    Pages 7-12

  4. Book chapterAbstract only

    Chapter 1.3 - The “Great Divide”

    Pages 13-19

  5. Book chapterAbstract only

    Chapter 1.4 - Demographics of Corporate Data

    Pages 21-25

  6. Book chapterAbstract only

    Chapter 1.5 - Corporate Data Analysis

    Pages 27-31

  7. Book chapterAbstract only

    Chapter 1.6 - The Life Cycle of Data: Understanding Data Over Time

    Pages 33-37

  8. Book chapterAbstract only

    Chapter 1.7 - A Brief History of Data

    Pages 39-45

  9. Book chapterAbstract only

    Chapter 2.1 - The End-State Architecture—The “World Map”

    Pages 47-57

  10. Book chapterAbstract only

    Chapter 3.1 - Transformations in the End-State Architecture

    Pages 59-66

  11. Book chapterAbstract only

    Chapter 4.1 - A Brief History of Big Data

    Pages 67-71

  12. Book chapterAbstract only

    Chapter 4.2 - What Is Big Data?

    Pages 73-80

  13. Book chapterAbstract only

    Chapter 4.3 - Parallel Processing

    Pages 81-87

  14. Book chapterAbstract only

    Chapter 4.4 - Unstructured Data

    Pages 89-97

  15. Book chapterAbstract only

    Chapter 4.5 - Contextualizing Repetitive Unstructured Data

    Pages 99-100

  16. Book chapterAbstract only

    Chapter 4.6 - Textual Disambiguation

    Pages 101-110

  17. Book chapterAbstract only

    Chapter 4.7 - Taxonomies

    Pages 111-119

  18. Book chapterAbstract only

    Chapter 5.1 - The Siloed Application Environment

    Pages 121-131

  19. Book chapterAbstract only

    Chapter 6.1 - Introduction to Data Vault 2.0

    Pages 133-140

  20. Book chapterAbstract only

    Chapter 6.2 - Introduction to Data Vault Modeling

    Pages 141-156

  21. Book chapterAbstract only

    Chapter 6.3 - Introduction to Data Vault Architecture

    Pages 157-162

  22. Book chapterAbstract only

    Chapter 6.4 - Introduction to Data Vault Methodology

    Pages 163-170

  23. Book chapterAbstract only

    Chapter 6.5 - Introduction to Data Vault Implementation

    Pages 171-176

  24. Book chapterAbstract only

    Chapter 7.1 - The Operational Environment: A Short History

    Pages 177-183

  25. Book chapterAbstract only

    Chapter 7.2 - The Standard Work Unit

    Pages 185-189

  26. Book chapterAbstract only

    Chapter 7.3 - Data Modeling for the Structured Environment

    Pages 191-198

  27. Book chapterAbstract only

    Chapter 8.1 - A Brief History of Data Architecture

    Pages 199-210

  28. Book chapterAbstract only

    Chapter 8.2 - Big Data/Existing System Interface

    Pages 211-218

  29. Book chapterAbstract only

    Chapter 8.3 - The Data Warehouse/Operational Environment Interface

    Pages 219-224

  30. Book chapterAbstract only

    Chapter 8.4 - Data Architecture: A High-Level Perspective

    Pages 225-229

  31. Book chapterAbstract only

    Chapter 9.1 - Repetitive Analytics: Some Basics

    Pages 231-249

  32. Book chapterAbstract only

    Chapter 9.2 - Analyzing Repetitive Data

    Pages 251-260

  33. Book chapterAbstract only

    Chapter 9.3 - Repetitive Analysis

    Pages 261-268

  34. Book chapterAbstract only

    Chapter 10.1 - Nonrepetitive Data

    Pages 269-289

  35. Book chapterAbstract only

    Chapter 10.2 - Mapping

    Pages 291-293

  36. Book chapterAbstract only

    Chapter 10.3 - Analytics From Nonrepetitive Data

    Pages 295-308

  37. Book chapterAbstract only

    Chapter 11.1 - Operational Analytics: Response Time

    Pages 309-317

  38. Book chapterAbstract only

    Chapter 12.1 - Operational Analytics

    Pages 319-329

  39. Book chapterAbstract only

    Chapter 13.1 - Personal Analytics

    Pages 331-335

  40. Book chapterAbstract only

    Chapter 14.1 - Data Models Across the End-State Architecture

    Pages 337-351

  41. Book chapterAbstract only

    Chapter 15.1 - The System of Record

    Pages 353-361

  42. Book chapterAbstract only

    Chapter 16.1 - Business Value and the End-State Architecture

    Pages 363-369

  43. Book chapterAbstract only

    Chapter 17.1 - Managing Text

    Pages 371-379

  44. Book chapterAbstract only

    Chapter 18.1 - An Introduction to Data Visualizations

    Pages 381-395

  45. Book chapterNo access

    Glossary

    Pages 397-408

  46. Book chapterNo access

    Index

    Pages 409-416

About the book

Description

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 Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

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 Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

Key Features

  • New case studies include expanded coverage of textual management and analytics
  • New chapters on visualization and big data
  • Discussion of new visualizations of the end-state architecture
  • New case studies include expanded coverage of textual management and analytics
  • New chapters on visualization and big data
  • Discussion of new visualizations of the end-state architecture

Details

ISBN

978-0-12-816916-2

Language

English

Published

2019

Copyright

Copyright © 2019 Elsevier Inc. All rights reserved.

Imprint

Academic Press

Authors

W.H. Inmon

Daniel Linstedt

Mary Levins