Developing High Quality Data Models
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Developing High Quality Data Models
Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool. This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling. - Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality - Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates - Develops ideas for creating consistent approaches to high quality data models
The Data Modeling Handbook
Author: Michael C. Reingruber
language: en
Publisher: John Wiley & Sons
Release Date: 1994-12-17
This practical, field-tested reference doesn't just explain the characteristics of finished, high-quality data models--it shows readers exactly how to build one. It presents rules and best practices in several notations, including IDEFIX, Martin, Chen, and Finkelstein. The book offers dozens of real-world examples and go beyond basic theory to provide users with practical guidance.
Data Modeling Essentials
If you are seeking expert tutelage for data modelling tools and techniques, you need look no further. Regardless of your level of expertise, as a data analyst, data modeler, data architect, database designer, database application developer, database administrator, business analysts, or systems designers, this book will serve as an invaluable resource in your effort to build reliable and effective data models. Beginning with the basics, this book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modelling and database design. Later chapters delve into advanced topics and enterprise data modelling, covering business rules, data warehousing, data migration, and more. This new and expanded edition updates existing content where current practice dictates and adds new content on Modelling XML, Master and Reference Data, Mapping Between Models, Data Migration, and other areas of intense interest to the data modelling community. NEW TO THIS EDITION • Enhanced contextual treatment of data modeling by providing more examples of data models and their quality in examining where the benefits derive. • NEW chapter on Master and Reference Data Management • NEW chapter of Data Migration • NEW chapter on modeling XML messages • NEW chapter on Mapping Between Data Models The perfect balance of theory and practice giving you both the foundation and the tools to develop high quality data models. Perfect reference for the reflective practitioner providing clear and accessible guidance to data modeling techniques. An invaluable resource containing vast amounts of useful and well illustrated information to those involved in data modeling, from the novice to the expert.