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Full Description
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.
Contents
1. An Introduction to Data Architecture2. The End-State Architecture - The "World Map"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations
