PDF Download NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software
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NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software
PDF Download NoSQL and SQL Data Modeling: Bringing Together Data, Semantics, and Software
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Product details
Paperback: 258 pages
Publisher: Technics Publications; First edition (March 11, 2016)
Language: English
ISBN-10: 1634621093
ISBN-13: 978-1634621090
Product Dimensions:
7.5 x 0.6 x 9.2 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
7 customer reviews
Amazon Best Sellers Rank:
#390,273 in Books (See Top 100 in Books)
This would have been a 5 star review if two things would have been addressed, one of content and one of format.The content would have been greatly enhanced if the example of the coffee shop went further into the actual implementation - with a top to bottom example that included actual document construction and related detail.The other issue is that the kindle version does not render some of the shapes. There were sections that would say something like 'the shape with the dashed line.' But the figure would be solid. I have the hard copy as well but people reading only the ki ndle version are going to be confused.
This is a solid conceptual overview of data modeling in general with advice for those looking toward combining SQL and NoSQL. (I suppose the title of the book is pretty apt.)
This is the definitive book for anyone seeking a modeling technique that works with the complexities of big data. There are so many things in the NoSQL world that just don't fit into entity-relationship and star-schema models. The first challenge is modeling to understand and describe existing data instead of modeling to design databases. But that is the reality of big data -- it exists without models and usually with insufficient metadata. Next we are challenged by data constructs that don't fit neatly into entity-relationship structures including many-to-many relationships, multi-valued attributes, embedded arrays, associations implemented without foreign key relationships, and much more. Key-value stores, document stores, graph databases, etc. just aren't easily described with ER modeling. Ted Hills has developed an extension of object modeling that he calls Concept and Object Modeling Notation (COMN) that elegantly steps up to the needs of big data modeling. He presents the notation and the modeling technique in a way that is clear and easily understood for a variety of people with different backgrounds and data experiences.Some thoughts from an earlier review and TDAN blog are included in the book description above. But I had the urge to say more. I had the opportunity to review the manuscript before publishing and to get to know Ted Hills. I learned from this book and I've been a data modeler for more than 30 years. Reading the book expanded my knowledge and getting to know Ted enriched my professional network. Good stuff all around!
Disclosure: Before this book was published, I met the author in person while attending his COMN course at a conference. I suggested to him that he should meet Steve Hoberman and see if Steve’s Technics Publications might publish it. It was neat to learn that Technics did so, and I was happy to see Ted’s note on that in his acknowledgements.This is a very specialized, yet indeed groundbreaking book on data modeling, and I believe it will serve as a valuable conceptual reference to keep data modeling abreast of current technology.NoSQL technologies continue to experience rapid increases in adoption, but their prescribed data structures, in order to scale along with these NoSQL distributed data systems, to unprecedented levels of data throughput, depart radically from classic physical implementations of entity-relational and dimension data models. As they continue to gain market adoption, undoubtedly there will be an increased need to establish common conceptual and logical data models, and then markedly different physical data models to represent the data physically in NoSQL technologies, in order to provide the benefits of a good data model: shared understanding, standardization, extensibility to related data subject areas, load performance, query performance, and a straightforward interoperability with relational databases, which continue to be the bulwark back-end for transactional processing.Unfortunately, but for very good reason, most or all traditional data modeling products cannot even express many prevalent NoSQL model patterns, such as indeterminate levels of nested sub-attributes, as well as situations where some records contain a different set of attributes than other records within a single table or file. The reason, of course, is that those NoSQL physically-instantiated model patterns themselves radically depart from the classic physical data model patterns in relational or dimensional, models. So, where does this leave the enterprise data architect who is supposed to establish a roadmap for enterprise data management? Where does that leave the typical data modeler – the vast majority of whom are familiar mostly with entity-relational modeling, but who is now supposed to design for interoperability between NoSQL and relational data? When this data modeler perceives that some of the NoSQL modeling notation looks more like UML (Unified Modeling Language), wherein objects, types and variables have a maddeningly divergent meaning than in entity-relationship data modeling, it only adds yet another, unfortunately long-standing area of confusion. Although it will surprise nobody to learn that early attempts by data modelers to establish common model patterns or design for logical interoperability between these disparate systems will not quickly bear fruit, the need for that interoperability and standardization will not go away simply because it is difficult.The point where we acknowledge that (1) the gap between RDBMS and NoSQL design is not superficial, that (2) it goes far deeper than simply a feature gap in the data modeling tools -- but rather in the underlying terminology and symbols of data modeling, and that (3) we must continue to build models to reap maximize value from our data solutions – is the point at which we turn to Ted Hills and this ground-breaking book. We simply cannot build the understandable data models that lead to well-designed, highly interoperable data systems, without doing so.Whether a reader is a relational data modeler, an object-oriented programmer, a UML-modeler, or a NoSQL Architect, Ted Hills guides each reader to a common set of concepts, and most specifically, the flexible and semantically robust Common Objective Modeling Notation (COMN) that Tedd Hills has invented.
I am less a data modeler and more of a programmer, so this was a very interesting way for me to become enlightened about the challenges that arise from not using a good data model and the opportunities to use a complete modeling language like COMN that does a much better job in our new world of big data. With the arrival of new unstructured data types, and more on the way, this book is a necessary reference for how to build big data solutions.This was all new thinking for me and somewhat challenging, but I liked this book because I got the sense of strong intellect on the other side gently introducing me to some very sophisticated new concepts. It is clear that Hills is a top expert in this field and has a real passion for his topic. I also really liked the way the book uses real world examples to show the limitations of current entity relationship modeling paradigms.A must read for anyone building big data solutions that they want to be robust, scalable and stand the test of time.
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