Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

  • 4h 27m
  • Daniel Linstedt, W.H. Inmon
  • Elsevier Science and Technology Books, Inc.
  • 2015

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
  • 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

About the Authors

Bill Inmon – the "father of data warehouse" – has written 53 books published in nine languages. Bill's latest adventure is the building of technology known as textual disambiguation – technology that reads raw text in a narrative format and allows the text to be placed in a conventional database so that it can be analyzed by standard analytical technology, thereby creating unique business value for Big Data/unstructured data. Bill was named by ComputerWorld as one of the ten most influential people in the history of the computer profession.

Dan Linstedt is an internationally known expert in data warehousing and business intelligence. He's worked in the field for more than 23 years, and continues to help Fortune 50 clients and government customers around the world in their pursuit of business information (BI) excellence. He's an expert in Big Data, unstructured data systems, and performance and tuning. He's also the author and inventor of the Data Vault Model and Methodology. He currently works with government agencies and major financial industries as a mentor for their enterprise BI initiatives.

In this Book

  • Corporate Data
  • The Data Infrastructure
  • The “Great Divide”
  • Demographics of Corporate Data
  • Corporate Data Analysis
  • The Life Cycle of Data – Understanding Data over Time
  • A Brief History of Data
  • A Brief History of Big Data
  • What is Big Data?
  • Parallel Processing
  • Unstructured Data
  • Contextualizing Repetitive Unstructured Data
  • Textual Disambiguation
  • Taxonomies
  • A Brief History of Data Warehouse
  • Integrated Corporate Data
  • Historical Data
  • Data Marts
  • The Operational Data Store
  • What a Data Warehouse is Not
  • Introduction to Data Vault
  • Introduction to Data Vault Modeling
  • Introduction to Data Vault Architecture
  • Introduction to Data Vault Methodology
  • Introduction to Data Vault Implementation
  • The Operational Environment – A Short History
  • The Standard Work Unit
  • Data Modeling for the Structured Environment
  • Metadata
  • Data Governance of Structured Data
  • A Brief History of Data Architecture
  • Big Data/Existing Systems Interface
  • The Data Warehouse/Operational Environment Interface
  • Data Architecture – A High-Level Perspective
  • Repetitive Analytics – Some Basics
  • Analyzing Repetitive Data
  • Repetitive Analysis
  • Nonrepetitive Data
  • Mapping
  • Analytics from Nonrepetitive Data
  • Operational Analytics
  • Operational Analytics
  • Personal Analytics
  • A Composite Data Architecture
  • Glossary