Data Architecture: A Primer for the Data Scientist

The first edition of Data Architecture was written more than five years ago and, since then 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 which 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 fits into the grand scheme of things. The Second Edition of Data Architecture: A Primer for the Data Scientist 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 is gathered and can be placed into an existing framework or architecture, it cannot be used to its 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 Reviews the exponential growth of Big Data integration and applications across industries - from healthcare to financePlaces new emphasis on end state architecture as a lens for understanding the architecture of big dataExplains how Big Data fits within an existing systems environment, as well as the value of data transformation and redundancyIncludes new chapters on Data lakes, ponds, landing zones; IoT, Edge computing; as well as data modelling and taxonomies INDICE: 1. Introduction to architecture2. Diagram of the world?, end state architecture3. Transformation and redundancy4. Big Data5. Siloed applications6. Data vault7. Data lake, ponds, landing zone8. IoT, Edge computing 9. Operational environment10. The evolution of data architecture 11. Repetitive data, the sandbox 12. Non-repetitive data, contextualization 13. Operational performance 14. Integration of data 15. Personal computing 16. Managing text, taxonomies 17. System of record 18. The intellectual roadmap - data modelling, taxonomies, etc. 19. Business value across the architecture 20. Virtualization, streaming 21. The end of evolution

• ISBN: 978-0-12-816916-2
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 450
• Fecha Publicación: 01/06/2019
• Nº Volúmenes: 1
• Idioma: Inglés