Data Architecture: A Primer for the Data Scientist: A Primer for the Data Scientist

by William H Inmon, Dan Linstedt, Mary Levins

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 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.

Genres: Nonfiction, Computer Science, Textbooks, Business

431 pages, Paperback

First published November 26, 2014

Reviews

[Joe · Rating 2 out of 5]

This book peaks early, where the author explains an "ideal" data architecture and all its components. He explains clearly why he thinks they all need to be there. There's also as good of a definition of "big data" as I've seen anywhere.

But I gave up on the later parts of the book because it got repetitive, except for very narrow issues.

[Lolo · Rating 2 out of 5]

First of all, I cannot believe someone like Inmon, the father of Data Warehouse, cannot draw a good diagram to illustrate his ideas. Or even hire one to do it for him! Almost all of the diagrams seem so rushed out. Many of them seem so pointless that i cannot understand why they were even included!

OK, rant over. Let's continue...

IMO, the most interesting part of the book was the "textual disambiguation" in order to appoint meaning to words extracted from emails and apply a context, but it was an abstract illustration of ideas.

Even though all the topics in this book are very important with many useful applications, there is a lack of examples and practical illustration of the ideas and techniques. Comparing this book to Kimball's The Data Warehouse Toolkit, I expected more practical examples, not silly abstract ill-drawn diagrams. It seems that the author leaves the practical/technical application of the ideas to the reader.

I wouldn't suggest this book to anyone. I would search for another book that illustrates the ideas in a more practical way.

[☘Misericordia☘ ⚡ϟ⚡⛈⚡☁ ❇️❤❣ · Rating 5 out of 5]

Takeouts TBA

[Aaron Stainback · Rating 4 out of 5]

Talk about repetitive data :)

This book was great but it repeated itself a lot. Overall I would recommend it though, great job on combining data lakes and data vault.