Graph Data Modeling for NoSQL and SQL: Visualize Structure and Meaning

Technics Publications
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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.

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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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Additional Information

Publisher
Technics Publications
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Published on
Sep 9, 2016
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Pages
214
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ISBN
9781634621236
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Language
English
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Genres
Computers / Data Modeling & Design
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Content Protection
This content is DRM protected.
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 How do we design for data when traditional design techniques cannot extend to new database technologies? In this era of big data and the Internet of Things, it is essential that we have the tools we need to understand the data coming to us faster than ever before, and to design databases and data processing systems that can adapt easily to ever-changing data schemas and ever-changing business requirements. There must be no intellectual disconnect between data and the software that manages it. It must be possible to extract meaning and knowledge from data to drive artificial intelligence applications. Novel NoSQL data organization techniques must be used side-by-side with traditional SQL databases. Are existing data modeling techniques ready for all of this?

The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases.

This book will teach you:

the simple and familiar graphical notation of COMN with its three basic shapes and four line styles how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that aren’t tangled with confused techno-speak how to express logical data designs that are freer from implementation considerations than is possible in any other notation how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development
​This book undertakes to marry the concepts of "Concept Mapping" with a "Design Thinking" approach in the context of business analysis. While in the past a lot of attention has been paid to the business process side, this book now focusses information quality and valuation, master data and hierarchy management, business rules automation and business semantics as examples for business innovation opportunities. The book shows how to take "Business Concept Maps" further as information models for new IT paradigms. In a way this books redefines and extends business analysis towards solutions that can be described as business synthesis or business development. Business modellers, analysts and controllers, as well as enterprise information architects, will benefit from the intuitive modelling and designing approach presented in this book. The pragmatic and agile methods presented can be directly applied to improve the way organizations manage their business concepts and their relationships.

"This book is a great contribution to the information management community. It combines a theoretical foundation with practical methods for dealing with important problems. This is rare and very useful. Conceptual models that communicate business reality effectively require some degree of creative imagination. As such, they combine the results of business analysis with communication design, as is extensively covered in this book."

Dr. Malcolm Chisholm, President at AskGet.com Inc.

“Truly understanding business requirements has always been a major stumbling block in business intelligence (BI) projects. In this book, Thomas Frisendal introduces a powerful technique—business concept mapping—that creates a virtual mind-meld between business users and business analysts. Frisendal does a wonderful explaining and demonstrating how this tool can improve the outcome of BI and other development projects ."

Wayne Eckerson, executive director, BI Leadership Forum

 Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation Read a data model of any size and complexity with the same confidence as reading a book Build a fully normalized relational data model, as well as an easily navigatable dimensional model Apply techniques to turn a logical data model into an efficient physical design Leverage several templates to make requirements gathering more efficient and accurate Explain all ten categories of the Data Model Scorecard Learn strategies to improve your working relationships with others Appreciate the impact unstructured data has, and will have, on our data modeling deliverables Learn basic UML concepts Put data modeling in context with XML, metadata, and agile development Book Review by Johnny Gay
In this book review, I address each section in the book and provide what I found most valuable as a data modeler. I compare, as I go, how the book's structure eases the new data modeler into the subject much like an instructor might ease a beginning swimmer into the pool.

This book begins like a Dan Brown novel. It even starts out with the protagonist, our favorite data modeler, lost on a dark road somewhere in France. In this case, what saves him isn't a cipher, but of all things, something that's very much like a data model in the form of a map! The author deems they are both way-finding tools.

The chapters in the book are divided into 5 sections. The chapters in each section end with an exercise and a list of the key points covered to reinforce what you've learned. I find myself comparing the teaching structure of the book to the way most of us learn to swim. 
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