This book will provide the business or IT professional with a practical working knowledge of data modelling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You will build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. The book combines real-world experience and best practices with down to earth advice, humour, and even cartoons to help you master the following ten objectives: Understand the basics of data modelling and relational theory, and how to apply these skills using CA ERwin Data Modeler; Read a data model of any size and complexity with the same confidence as reading a book; Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin's Data Modelers Design Layer Architecture. The objectives includes : Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both top down and bottom-up design; Learn how to create reusable domains, naming standards, UDPs, and model templates in CA ERwin Data Modeler to reduce modelling time, improve data quality, and increase enterprise consistency; Share data model information with various audiences using model formatting and layout techniques, reporting, and metadata exchange; Use the new workspace customisation features in CA ERwin Data Modeler r8 to create a workflow suited to your own individual needs. The objectives covered includes: Leverage the new Bulk Editing features in CA ERwin Data Modeler r8 for mass metadata updates, as well as import/export with Microsoft Excel; Compare and merge model changes using CA ERwin Data Modelers Complete Compare features; and, Optimise the organisation and layout of your data models through the use of Subject Areas, Diagrams, Display Themes, and more. Section I provides an overview of data modelling: what it is, and why it is needed. The basic features of CA ERwin Data Modeler are introduced with a simple, easy-to-follow example. Section II introduces the basic building blocks of a data model, including entities, relationships, keys, and more. How-to examples using CA ERwin Data Modeler are provided for each of these building blocks, as well as 'real world' scenarios for context. Section III covers the creation of reusable standards, and their importance in the organisation. From standard data modelling constructs such as domains to CA ERwin-specific features such as UDPs, this section covers step-by-step examples of how to create these standards in CA ERwin Data Modeling, from creation, to template building, to sharing standards with end users through reporting and queries. Section IV discusses conceptual, logical, and physical data models, and provides a comprehensive case study using CA ERwin Data Modeler to show the interrelationships between these models using CA ERwin's Design Layer Architecture. Real world examples are provided from requirements gathering, to working with business sponsors, to the hands-on nitty-gritty details of building conceptual, logical, and physical data models with CA ERwin Data Modeler r8.

Did you ever try getting Business and IT to agree on the project scope for a new application? Or try getting the Sales & Marketing department to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse -- without them dozing off? You can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives. Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach. Know how to evaluate toolsets for building and storing your models. Practice exercises and walk through a case study to reinforce your modelling skills.