Data Modeling Made Simple with Embarcadero ER/Studio Data Architect: Adapting to Agile Data Modeling in a Big Data World
Author: Steve Hoberman (Author)
Publisher finelybook 出版社: Technics Publications
Edition 版次: Second
Publication Date 出版日期: 2015-11-06
Language 语言: English
Print Length 页数: 342 pages
ISBN-10: 1634620925
ISBN-13: 9781634620925
Book Description
By finelybook
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 NoSQL, such as MongoDB’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 NoSQL 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 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.
From the Inside Flap
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/Studios support for agile development, as well as a description of some of ER/Studios newer features for NoSQL, such as MongoDBs containment structure. You will build many ER/Studio data models along the way, applying best practices to master these ten objectives: 1. Know why a data model is needed and which ER/Studio models are the most appropriate for each situation 2. Understand each component on the data model and how to represent and create them in ER/Studio 3. Know how to leverage ER/Studios latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases 4. Know how to apply all the foundational features of ER/Studio 5. Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio 6. Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design 7. Improve data model quality and impact analysis results by leveraging ER/Studios lineage functionality and compare/merge utility 8. Be able to apply ER/Studios data dictionary features 9. Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats 10. Leverage ER/Studios 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 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.
From the Back Cover
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 NoSQL, such as MongoDB’s containment structure. You will build many ER/Studio data models along the way, applying best practices to master these ten objectives:
1.Know why a data model is needed and which ER/Studio models are the most appropriate for each situation
2.Understand each component on the data model and how to represent and create them in ER/Studio
3.Know how to leverage ER/Studio’s latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases
4.Know how to apply all the foundational features of ER/Studio
5.Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio
6.Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design
7.Improve data model quality and impact analysis results by leveraging ER/Studio’s lineage functionality and compare/merge utility
8.Be able to apply ER/Studio’s data dictionary features
9.Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats
10.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 sectio
About the Author
Steve Hoberman has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style, and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conference, recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, and highest rated tutorial presenter at Enterprise Data World 2014 and Enterprise Data World 2015.
