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.
1. Chapter 1.1: An Introduction to Data Architecture - p. v 2. Chapter 1.2: The Data Infrastructure - p. 5 3. Chapter 1.3: The “Great Divide” - p. 12 4. Chapter 1.4: Demographics of Corporate Data - p. 19 5. Chapter 1.5: Corporate Data Analysis - p. 25 6. Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time - p. 31 7. Chapter 1.7: A Brief History of Data - p. 37 8. Chapter 2.1: The End-State Architecture—The “World Map” - p. 45 9. Chapter 3.1: Transformations in the End-State Architecture - p. 57 10. Chapter 4.1: A Brief History of Big Data - p. 66 11. Chapter 4.2: What Is Big Data? - p. 71 12. Chapter 4.3: Parallel Processing - p. 80 13. Chapter 4.4: Unstructured Data - p. 87 14. Chapter 4.5: Contextualizing Repetitive Unstructured Data - p. 97 15. Chapter 4.6: Textual Disambiguation - p. 100 16. Chapter 4.7: Taxonomies - p. 110 17. Chapter 5.1: The Siloed Application Environment - p. 119 18. Chapter 6.1: Introduction to Data Vault 2.0 - p. 131 19. Chapter 6.2: Introduction to Data Vault Modeling - p. 140 20. Chapter 6.3: Introduction to Data Vault Architecture - p. 156 21. Chapter 6.4: Introduction to Data Vault Methodology - p. 162 22. Chapter 6.5: Introduction to Data Vault Implementation - p. 170 23. Chapter 7.1: The Operational Environment: A Short History - p. 176 24. Chapter 7.2: The Standard Work Unit - p. 183 25. Chapter 7.3: Data Modeling for the Structured Environment - p. 189 26. Chapter 8.1: A Brief History of Data Architecture - p. 198 27. Chapter 8.2: Big Data/Existing System Interface - p. 210 28. Chapter 8.3: The Data Warehouse/Operational Environment Interface - p. 218 29. Chapter 8.4: Data Architecture: A High-Level Perspective - p. 224 30. Chapter 9.1: Repetitive Analytics: Some Basics - p. 229 31. Chapter 9.2: Analyzing Repetitive Data - p. 249 32. Chapter 9.3: Repetitive Analysis - p. 260 33. Chapter 10.1: Nonrepetitive Data - p. 268 34. Chapter 10.2: Mapping - p. 289 35. Chapter 10.3: Analytics From Nonrepetitive Data - p. 293 36. Chapter 11.1: Operational Analytics: Response Time - p. 308 37. Chapter 12.1: Operational Analytics - p. 317 38. Chapter 13.1: Personal Analytics - p. 329 39. Chapter 14.1: Data Models Across the End-State Architecture - p. 335 40. Chapter 15.1: The System of Record - p. 351 41. Chapter 16.1: Business Value and the End-State Architecture - p. 361 42. Chapter 17.1: Managing Text - p. 369 43. Chapter 18.1: An Introduction to Data Visualizations - p. 379 44. Glossary - p. 395 45. Index - p. 409
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.
1. Chapter 1.1: An Introduction to Data Architecture - p. v 2. Chapter 1.2: The Data Infrastructure - p. 5 3. Chapter 1.3: The “Great Divide” - p. 12 4. Chapter 1.4: Demographics of Corporate Data - p. 19 5. Chapter 1.5: Corporate Data Analysis - p. 25 6. Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time - p. 31 7. Chapter 1.7: A Brief History of Data - p. 37 8. Chapter 2.1: The End-State Architecture—The “World Map” - p. 45 9. Chapter 3.1: Transformations in the End-State Architecture - p. 57 10. Chapter 4.1: A Brief History of Big Data - p. 66 11. Chapter 4.2: What Is Big Data? - p. 71 12. Chapter 4.3: Parallel Processing - p. 80 13. Chapter 4.4: Unstructured Data - p. 87 14. Chapter 4.5: Contextualizing Repetitive Unstructured Data - p. 97 15. Chapter 4.6: Textual Disambiguation - p. 100 16. Chapter 4.7: Taxonomies - p. 110 17. Chapter 5.1: The Siloed Application Environment - p. 119 18. Chapter 6.1: Introduction to Data Vault 2.0 - p. 131 19. Chapter 6.2: Introduction to Data Vault Modeling - p. 140 20. Chapter 6.3: Introduction to Data Vault Architecture - p. 156 21. Chapter 6.4: Introduction to Data Vault Methodology - p. 162 22. Chapter 6.5: Introduction to Data Vault Implementation - p. 170 23. Chapter 7.1: The Operational Environment: A Short History - p. 176 24. Chapter 7.2: The Standard Work Unit - p. 183 25. Chapter 7.3: Data Modeling for the Structured Environment - p. 189 26. Chapter 8.1: A Brief History of Data Architecture - p. 198 27. Chapter 8.2: Big Data/Existing System Interface - p. 210 28. Chapter 8.3: The Data Warehouse/Operational Environment Interface - p. 218 29. Chapter 8.4: Data Architecture: A High-Level Perspective - p. 224 30. Chapter 9.1: Repetitive Analytics: Some Basics - p. 229 31. Chapter 9.2: Analyzing Repetitive Data - p. 249 32. Chapter 9.3: Repetitive Analysis - p. 260 33. Chapter 10.1: Nonrepetitive Data - p. 268 34. Chapter 10.2: Mapping - p. 289 35. Chapter 10.3: Analytics From Nonrepetitive Data - p. 293 36. Chapter 11.1: Operational Analytics: Response Time - p. 308 37. Chapter 12.1: Operational Analytics - p. 317 38. Chapter 13.1: Personal Analytics - p. 329 39. Chapter 14.1: Data Models Across the End-State Architecture - p. 335 40. Chapter 15.1: The System of Record - p. 351 41. Chapter 16.1: Business Value and the End-State Architecture - p. 361 42. Chapter 17.1: Managing Text - p. 369 43. Chapter 18.1: An Introduction to Data Visualizations - p. 379 44. Glossary - p. 395 45. Index - p. 409
