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Bibliographic Detail
Publisher
Technics Publications
Publication date
September 19, 2016
Pages
300
Binding
Paperback
ISBN-13
9781634621212
ISBN-10
1634621212
Dimensions
0.49 by 7.50 by 9.25 in.
Weight
0.93 lbs.
Original list price
$59.95
Amazon.com says people who bought this book also bought:
Learning Neo4j 3.x - Second Edition: Effective data modeling, performance tuning and data visualization techniques in Neo4j | DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition) | Nosql and SQL Data Modeling | Graph Databases | Introduction to Graph Theory | Enterprise Model Patterns | Neo4j Graph Data Modeling
Learning Neo4j 3.x - Second Edition: Effective data modeling, performance tuning and data visualization techniques in Neo4j | DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition) | Nosql and SQL Data Modeling | Graph Databases | Introduction to Graph Theory | Enterprise Model Patterns | Neo4j Graph Data Modeling
Summaries and Reviews
Amazon.com description: Product Description: 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:
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:
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?
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:
- The advances in applied cognitive psychology address the needs for proper contextual framework and for better communication, also in data modeling, and
- The rapid intake of non-relational technologies (Big Data and NoSQL).
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