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Data Architecture: A Primer for the Data Scientist. Edition No. 2

  • ID: 4720916
  • Book
  • 431 Pages
  • Elsevier Science and Technology
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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.

  • 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

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1. An Introduction to Data Architecture 2. The End-State Architecture
The "World Map" 3. Transformations in the End-State Architecture 4. A Brief History of Big Data 5. The Siloed Application Environment 6. Introduction to Data Vault 2.0 7. The Operational Environment: A Short History 8. A Brief History of Data Architecture 9. Repetitive Analytics: Some Basics 10. Nonrepetitive Data 11. Operational Analytics: Response Time 12. Operational Analytics 13. Personal Analytics 14. Data Models Across the End-State Architecture 15. The System of Record 16. Business Value and the End-State Architecture 17. Managing Text 18. An Introduction to Data Visualizations

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Inmon, W.H.
Best known as the "Father of Data Warehousing," Bill Inmon has become the most prolific and well-known author worldwide in the big data analysis, data warehousing and business intelligence arena. In addition to authoring more than 50 books and 650 articles, Bill has been a monthly columnist with the Business Intelligence Network, EIM Institute and Data Management Review. In 2007, Bill was named by Computerworld as one of the "Ten IT People Who Mattered in the Last 40 Years” of the computer profession. Having 35 years of experience in database technology and data warehouse design, he is known globally for his seminars on developing data warehouses and information architectures. Bill has been a keynote speaker in demand for numerous computing associations, industry conferences and trade shows. Bill Inmon also has an extensive entrepreneurial background: He founded Pine Cone Systems, later named Ambeo in 1995, and founded, and took public, Prism Solutions in 1991. Bill consults with a large number of Fortune 1000 clients, and leading IT executives on Data Warehousing, Business Intelligence, and Database Management, offering data warehouse design and database management services, as well as producing methodologies and technologies that advance the enterprise architectures of large and small organizations world-wide. He has worked for American Management Systems and Coopers & Lybrand. Bill received his Bachelor of Science degree in Mathematics from Yale University, and his Master of Science degree in Computer Science from New Mexico State University.
Linstedt, Daniel
Dan Linstedt has more than 25 years of experience in the Data Warehousing and Business Intelligence field and is internationally known for inventing the Data Vault 1.0 model and the Data Vault 2.0 System of Business Intelligence. He helps business and government organizations around the world to achieve BI excellence by applying his proven knowledge in Big Data, unstructured information management, agile methodologies and product development. He has held training classes and presented at TDWI, Teradata Partners, DAMA, Informatica, Oracle user groups and Data Modeling Zone conference. He has a background in SEI/CMMI Level 5, and has contributed architecture efforts to petabyte scale data warehouses and offers high quality on-line training and consulting services for Data Vault.
Levins, Mary
Mary Levins is recognized as a leader in Data Governance with over 20 years of experience working with organizations to bring value through data strategies that drive business results. Mary has a BS and MS in Industrial Engineering, and her experience spans across many different industries including manufacturing, healthcare, energy/utilities, automotive, electronics, and financial (including Consumer Credit Bureaus and Credit Unions). Today, Mary is the founder of Sierra Creek Consulting, a specialized firm delivering Data Governance, Data Management, and Data Solutions to help companies bring value through data.
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