Technics Publications Data modeling made simple with er/studio data architect: adapting to agile data modeling in a big data world, paperback/steve hoberman

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Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio.

This second edition includes numerous updates and new sections including an overview of ER/Studio's support for agile development, as well as a description of some of ER/Studio's newer features for No SQL, such as Mongo DB's containment structure.

You will build many ER/Studio data models along the way, applying best practices to master these ten objectives: Know why a data model is needed and which ER/Studio models are the most appropriate for each situation Understand each component on the data model and how to represent and create them in ER/Studio Know how to leverage ER/Studio's latest features including those assisting agile teams and forward and reverse engineering of No SQL databases Know how to apply all the foundational features of ER/Studio Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design Improve data model quality and impact analysis results by leveraging ER/Studio's lineage functionality and compare/merge utility Be able to apply ER/Studio's data dictionary features Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats Leverage ER/Studio's naming functionality to improve naming consistency, including the new Automatic Naming Translation feature.

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 learn about the newest features in ER/Studio (including features on big data and agile), and the ER/Studio environment.

By the end of this section, you will have created and saved.