Title page
by Mary Levins, Daniel Linstedt, W.H. Inmon
Data Architecture: A Primer for the Data Scientist, 2nd Edition
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Chapter 1.1: An Introduction to Data Architecture
- Chapter 1.2: The Data Infrastructure
- Chapter 1.3: The “Great Divide”
- Chapter 1.4: Demographics of Corporate Data
- Chapter 1.5: Corporate Data Analysis
- Chapter 1.6: The Life Cycle of Data: Understanding Data Over Time
- Chapter 1.7: A Brief History of Data
- Chapter 2.1: The End-State Architecture—The “World Map”
- Abstract
- Architectural Components
- Different Kinds of Data in the End State Architecture
- Shaping the Data Through Models
- Where Is the Data Warehouse?
- Where Different Types of Questions Are Answered Across the End State Architecture
- Data in the Data Lake
- Metadata in the End State Architecture
- Networked Metadata
- An Evolutionary Experience
- The Data Lake Architecture
- Chapter 3.1: Transformations in the End-State Architecture
- Chapter 4.1: A Brief History of Big Data
- Chapter 4.2: What Is Big Data?
- Chapter 4.3: Parallel Processing
- Chapter 4.4: Unstructured Data
- Chapter 4.5: Contextualizing Repetitive Unstructured Data
- Chapter 4.6: Textual Disambiguation
- Chapter 4.7: Taxonomies
- Abstract
- Data Models/Taxonomies
- Applicability of Taxonomies
- What Is a Taxonomy?
- Taxonomies in Multiple Languages
- Commercial or Private Taxonomies?
- Dynamics of Taxonomies and Textual Disambiguation
- Taxonomies and Textual Disambiguation—Separate Technologies
- Different Types of Taxonomies
- Taxonomies—Maintenance Over Time
- Chapter 5.1: The Siloed Application Environment
- Chapter 6.1: Introduction to Data Vault 2.0
- Chapter 6.2: Introduction to Data Vault Modeling
- Abstract
- What Is a Data Vault Model Concept?
- Data Vault Model Defined
- Components of a Data Vault Model
- What Makes Business Keys So Interesting?
- What Does This Have to Do With Data Vault and Data Warehousing?
- How Does This Translate to Data Vault Modeling?
- Why Restructure the Data From the Staging Area?
- What Are the Basic Rules of the Data Vault Model?
- Why Do We Need Many to Many Link Structures?
- Primary Key Options for Data Vault 2.0
- Chapter 6.3: Introduction to Data Vault Architecture
- Chapter 6.4: Introduction to Data Vault Methodology
- Abstract
- Data Vault 2.0 Methodology Overview
- How Does CMMI Contribute to the Methodology?
- If CMMI Is So Great, Why Should We Care About Agility Then?
- Why Include PMP, SDLC If CMMI and Agile Should Be All That's Needed?
- So Then, What Does Six Sigma Contribute to the Data Vault 2 Methodology?
- Where Does TQM (Total Quality Management) Fit in to All of This?
- Chapter 6.5: Introduction to Data Vault Implementation
- Chapter 7.1: The Operational Environment: A Short History
- Chapter 7.2: The Standard Work Unit
- Chapter 7.3: Data Modeling for the Structured Environment
- Chapter 8.1: A Brief History of Data Architecture
- Chapter 8.2: Big Data/Existing System Interface
- Abstract
- The Big Data/Existing Systems Interface
- The Repetitive Raw Big Data/Existing Systems Interface
- Exception Based Data
- The Nonrepetitive Raw Big Data/Existing Systems Interface
- Into the Existing Systems Environment
- The “Context Enriched” Big Data Environment
- Analyzing Structured Data/Unstructured Data Together
- Chapter 8.3: The Data Warehouse/Operational Environment Interface
- Chapter 8.4: Data Architecture: A High-Level Perspective
- Chapter 9.1: Repetitive Analytics: Some Basics
- Abstract
- Different Kinds of Analysis
- Looking for Patterns
- Heuristic Processing
- Freezing Data
- The Sandbox
- The “Normal” Profile
- Distillation, Filtering
- Subsetting Data
- Bias of the Sample
- Filtering Data
- Repetitive Data and Context
- Linking Repetitive Records
- Log Tape Records
- Analyzing Points of Data
- Outliers
- Data Over Time
- Chapter 9.2: Analyzing Repetitive Data
- Chapter 9.3: Repetitive Analysis
- Chapter 10.1: Nonrepetitive Data
- Abstract
- Inline Contextualization
- Taxonomy/Ontology Processing
- Custom Variables
- Homographic Resolution
- Acronym Resolution
- Negation Analysis
- Numeric Tagging
- Date Tagging
- Date Standardization
- List Processing
- Associative Word Processing
- Stop Word Processing
- Word Stemming
- Document Metadata
- Document Classification
- Proximity Analysis
- Functional Sequencing Within Textual ETL
- Internal Referential Integrity
- Preprocessing, Postprocessing
- Chapter 10.2: Mapping
- Chapter 10.3: Analytics From Nonrepetitive Data
- Chapter 11.1: Operational Analytics: Response Time
- Chapter 12.1: Operational Analytics
- Chapter 13.1: Personal Analytics
- Chapter 14.1: Data Models Across the End-State Architecture
- Chapter 15.1: The System of Record
- Abstract
- The End User Cycle of Awareness
- The System of Record
- The System of Record in the End State Architecture
- The Role of Age in the System of Record
- A Simple Example
- The Flow of Data in the System of Record
- Other Data Than the System of Record
- Is Data Updated in the System of Record?
- Detailed and Summary Data in the System of Record
- Auditing Data and the System of Record
- Text and the System of Record
- Chapter 16.1: Business Value and the End-State Architecture
- Chapter 17.1: Managing Text
- Chapter 18.1: An Introduction to Data Visualizations
- Glossary
- Index
Data Architecture
A Primer for the Data Scientist
Second Edition
W.H. Inmon
Daniel Linstedt
Mary Levins
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