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

Master a graph data modelling technique superior to traditional data modelling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs. From Karen Lopezs Foreword: In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modelling 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 modelling, enterprise data models and traditional modelling 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 modelling may or may not fit in business modelling. The main subject of this work is a proposal for a new exploration-driven modelling approach and new modelling 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 modelling data. From the authors introduction: This book proposes a new approach to data modelling -- one that turns the inside out. For well over thirty years, relational modelling and normalisation was the name of the game. One can ask that if normalisation was the answer, what was the problem? There is something upside-down in that approach, as we will see in this book. Data analysis (modelling) 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 visualisation (again perception and cognition) rather than pure mathematical ability. Two compelling events make a paradigm shift in data modelling 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 modelling, and 2. The rapid intake of non-relational technologies (Big Data and NoSQL).