Data Architecture: A Primer for the Data Scientist
Mary Levins, Daniel Linstedt, W.H. Inmon
Sold by Rarewaves USA United, OSWEGO, IL, U.S.A.
AbeBooks Seller since 20 June 2025
New - Soft cover
Condition: New
Quantity: 2 available
Add to basketSold by Rarewaves USA United, OSWEGO, IL, U.S.A.
AbeBooks Seller since 20 June 2025
Condition: New
Quantity: 2 available
Add to basketOver 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.
Seller Inventory # LU-9780128169162
Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, Second Edition, addresses how Big Data fits within the existing information infrastructure and data warehousing systems. This is an essential topic as researchers and engineers increasingly need to deal with large and complex sets of data. Until data is gathered and 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 industries, the authors explain where Big Data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.
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