Dedication
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
Dedication
This book is dedicated to the doctors and the hospitals who literally saved my life. This book would have never been written without the doctors and the hospital and their fine care:
- Rose Hospital, Denver, Colorado
- National Jewish Hospital, Denver, Colorado
- Dr Christopher Stees, Rose Hospital
- Dr Peder Horner, Rose Hospital
- Dr Michael Firstenberg, Rose Hospital
- Ryan Tobin, Rose Hospital
- Dr Susan Kotake, National Jewish Hospital
- Dr Ellen Volker, National Jewish Hospital
- and to all the nurses and other staff who are too many to mention.
- Thank you, thank you, thank you
- WHI Feb 2019.
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.