Data Modeling Made Simple with ER/Studio Data Architect
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More About This Title Data Modeling Made Simple with ER/Studio Data Architect


Data Modeling Made Simple with ER/Studio Data Architect will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. You'll build many ER/Studio data models along the way, applying best practices to master these ten objectives:

1. You will know why a data model is needed and which ER/Studio models are the most appropriate for each situation
2. You will be able to read a data model of any size and complexity with the same confidence as reading a book
3. You will know how to apply all the key features of ER/Studio
4. You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio
5. You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design
6. You will improve data model quality and impact analysis results by leveraging ER/Studio’s lineage functionality and compare/merge utility
7. You will achieve enterprise architecture through ER/Studio’s repository and portal functionality
8. You will be able to apply ER/Studio’s data dictionary features
9. You will learn ways of sharing the data model through reporting and through exporting the model in a variety of formats
10. You will leverage ER/Studio’s naming functionality to improve naming consistency

This book contains four sections:
Section I introduces data modeling and the ER/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will also learn about the ER/Studio environment. By the end of this section, you will have created and saved your first data model in ER/Studio and be ready to start modeling in Section II!

Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to ‘read’ a data model of any size or complexity, and create a complete data model in ER/Studio.

Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio.

Section IV discusses additional features of ER/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.


Steve Hoberman is the most requested data modeling instructor in the world. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modeling with minimal investment. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then. Steve is the author of seven books on data modeling, the founder of the Design Challenges group, inventor of the Data Model Scorecard, Conference Chair of the Data Modeling Zone conference, and recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award. Steve can be reached at [email protected], @DataMdlRockStar on Twitter, or through Steve Hoberman on Linked-In.