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Graph Data Modeling for NoSQL and SQL, by Thomas Frisendal
Download PDF Graph Data Modeling for NoSQL and SQL, by Thomas Frisendal
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From the Inside Flap
Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs.From Karen Lopez’s Foreword: In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modeling notations and approaches: • Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? • Are ERDs relevant in the new world of Big Data? • Are ERDs still the best way to work with business users to understand their needs? • Are Logical and Physical Data Models too closely coupled? • Are we correct in using the same notations for communicating with business users and developers? • Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? • What new notations and approaches will we need? • How will we use those to build enterprise database systems? Frisendal takes us through the history of data modeling, enterprise data models and traditional modeling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modeling may or may not fit in business modeling. The main subject of this work is a proposal for a new exploration-driven modeling approach and new modeling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modeling data.From the author’s introduction: This book proposes a new approach to data modeling—one that “turns the inside out”. For well over thirty years, relational modeling and normalization was the name of the game. One can ask that if normalization was the answer, what was the problem? There is something upside-down in that approach, as we will see in this book.Data analysis (modeling) is much like exploration. Almost literally. The data modeler wanders around searching for structure and content. It requires perception and cognitive skills, supported by intuition (a psychological phenomenon), that together determine how well the landscape of business semantics is mapped. Mapping is what we do; we explore the unknowns, draw the maps and post the “Here be Dragons” warnings. Of course there are technical skills involved, and surprisingly, the most important ones come from psychology and visualization (again perception and cognition) rather than pure mathematical ability. Two compelling events make a paradigm shift in data modeling possible, and also necessary: 1. The advances in applied cognitive psychology address the needs for proper contextual framework and for better communication, also in data modeling, and 2. The rapid intake of non-relational technologies (Big Data and NoSQL).
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About the Author
Thomas Frisendal is an experienced database consultant with more than 30 years on the IT vendor side and as an independent consultant. He has worked with databases and data modeling since the late 70s; since 1995 primarily on data warehouse projects. His area of excellence lies within the art of turning data into information and knowledge. Since 2005 he has specialized in business analysis, concept "harvesting" and mapping, modeling of information and data as well as design of analytical solutions. His approach to information-driven analysis and design is "New Nordic" in the sense that it represents the traditional Nordic values such as superior quality, functionality, reliability and innovation by new ways of communicating the structure and meaning of the business context. Thomas is an active writer and speaker, and at the time of writing also Chief Data Warehouse Architect at SimCorp. Thomas lives in Copenhagen, Denmark.
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Product details
Paperback: 300 pages
Publisher: Technics Publications; First edition (September 19, 2016)
Language: English
ISBN-10: 1634621212
ISBN-13: 978-1634621212
Product Dimensions:
7.5 x 0.5 x 9.2 inches
Shipping Weight: 15.5 ounces (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#1,068,687 in Books (See Top 100 in Books)
Perfect book which can give you requirement level expectation for Smart Data Lake which can be crawlable by machine, well understood by business and flexible enough for business changes and other details. In newer world where business is looking for insight to run new future strategy based upon partnership and collaboration and establish the humanism aspect by better understanding customer behavior this is a starting point.... New Generation Smart Data Model....Newer world Metadata is the data and data is the platform.
Data modeling goes all the way back to the beginning of the computer age, and Frisendal leads us on a fascinating journey from there to today's world, where the topic receives both support and dismissal in almost equal measure. I've long been a proponent of data modeling (although I wish they had called it "information modeling") and this book provides an excellent set of reasoning why. It also shows how we in the IT world have largely hijacked modeling, moving it to an engineering approach that is counter-intuitive for most business people.At a time when advocates of big data, data lakes and NoSQL tend to dismiss data modeling as unnecessary or, at best, of limited use, Frisendal turns to these new environments to show the power of data modeling and the different approaches possible and, indeed, necessary at the different levels--conceptual, logical and physical--of design.
In this elegant and approachable book, the author describes a pragmatic approach for modeling data with graphs. The approach can be viewed as a post-relational modeling technique, and can be applied not only with various NoSQL databases, but also with relational stores. Even beyond that, one can apply it for conceptual or domain modeling. This book fills a gap that exists between the traditional data modeling techniques and the emergence of new database models. The author rightfully puts a great value in visualization of models and uses the approach that is based on graph techniques. This is a valuable book not only for NoSQL and graph database practitioners, but also for anyone who wants to produce better models for nontrivial applications and data stores. The author also provides an excellent review and history of various modeling techniques. Highly recommended: delightful and immediately usable.
It jumps around, repeats itself alot, and has very little new value for anyone who has translated white board diagrams into SQL or NoSQL before. I was hoping for more complex examples that showcased graph advantages and disadvantages -- I got an obvious graph representation of NorthWind where the resulting SQL version remains the same, the NoSQL version uses aggregate roots, and the neo4j version copies the graph (obvious to any experienced software engineer).Edit: amending to 3 stars -- in hindsight and after re-reading through the description more thoroughly, it is clear that it's trying to answer something else and that the title is somewhat misleading.
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