Data Modeling Master Class Training Manual
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Data Modeling Master Class Training Manual
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Author : Steve Hoberman
language : en
Publisher:
Release Date : 2015-07
Data Modeling Master Class Training Manual written by Steve Hoberman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07 with categories.
This is the sixth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard. You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 10 Objectives 1.Explain data modeling components and identify them on your projects by following a question-driven approach 2.Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3.Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard 4.Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5.Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6.Practice finding structural soundness issues and standards violations 7.Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8.Use a series of templates for capturing and validating requirements, and for data profiling 9.Evaluate definitions for clarity, completeness, and correctness 10.Leverage the Data Vault and enterprise data model for a successful
Data Modeling Master Class Training Manual 5th Edition
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Author : Steve Hoberman
language : en
Publisher: Technics Publications, LLC
Release Date : 2014
Data Modeling Master Class Training Manual 5th Edition written by Steve Hoberman and has been published by Technics Publications, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Data structures (Computer science) categories.
This is the fifth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard . You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects.
Data Modeling Master Class Training Manual
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Author : Steve Hoberman
language : en
Publisher:
Release Date : 2019-05-14
Data Modeling Master Class Training Manual written by Steve Hoberman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-14 with categories.
This is the eighth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard(R). You will know not just how to build a data model, but how to build a data model well. Three case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 5 Objectives Determine how and when to use each data modeling component Apply techniques to elicit data requirements as a prerequisite to building a data model Build relational and dimensional conceptual, logical, and physical data models Incorporate supportability and extensibility features into the data model Assess the quality of a data model.
Data Modeling Master Class Training Manual 7th Edition
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Author : Steve Hoberman
language : en
Publisher:
Release Date : 2017-06
Data Modeling Master Class Training Manual 7th Edition written by Steve Hoberman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06 with Computers categories.
This is the seventh edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard(R). You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 10 Objectives 1. Explain data modeling components and identify them on your projects by following a question-driven approach 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book 3. Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard(R) 4. Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions 6. Practice finding structural soundness issues and standards violations 7. Recognize when to use abstraction and where patterns and industry data models can give us a great head start 8. Use a series of templates for capturing and validating requirements, and for data profiling 9. Evaluate definitions for clarity, completeness, and correctness 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.
Data Modeling Master Class Training Manual
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Author : Steve Hoberman
language : en
Publisher: Technics Publications, LLC
Release Date : 2011
Data Modeling Master Class Training Manual written by Steve Hoberman and has been published by Technics Publications, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Data structures (Computer science) categories.
This is the third edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com.
Data Modeling Master Class Training Manual 2nd Edition
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Author : Steve Hoberman
language : en
Publisher: Technics Publications LLC
Release Date : 2011
Data Modeling Master Class Training Manual 2nd Edition written by Steve Hoberman and has been published by Technics Publications LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Data structures (Computer science) categories.
A training manual for the Data Modelling Master Class. It includes a course on requirements gathering and data modelling, containing four days of practical techniques for producing solid relational and dimensional data models.
Data Modeling Master Class Training Manual 9th Edition
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Author : Steve Hoberman
language : en
Publisher:
Release Date : 2021
Data Modeling Master Class Training Manual 9th Edition written by Steve Hoberman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This is the ninth edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman's website, stevehoberman.com.
Data Modeling Fundamentals
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Author : Steve Hoberman
language : en
Publisher:
Release Date : 2018
Data Modeling Fundamentals written by Steve Hoberman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"The Data Modeling Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. This video contains a majority of the content from the first module in this course. For more on the Data Modeling Master Class, please visit SteveHoberman.com. This video provides an introduction into the field of data modeling by defining data model concepts and terms, along with why the data modeling process is so important and warnings of pitfalls to avoid. Shortly after the video starts, you will complete a very important exercise illustrating the four important gaps filled by data models. Next, we will explain data modeling concepts and terminology including entities, attributes, relationships, candidate keys, and subtypes, and provide you with a set of questions you can ask to quickly and precisely build a data model. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book. We will complete several exercises, including one on creating a data model based upon an existing set of data."--Resource description page.
Data Modeling For The Business
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Author : Steve Hoberman
language : en
Publisher: Technics Publications Llc
Release Date : 2009
Data Modeling For The Business written by Steve Hoberman and has been published by Technics Publications Llc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
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.
Data Model Scorecard
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Author : Steve Hoberman
language : en
Publisher: Technics Publications
Release Date : 2015-11-01
Data Model Scorecard written by Steve Hoberman and has been published by Technics Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-01 with Computers categories.
Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it’s essential to get the data model right. But how do you determine right? That’s where the Data Model Scorecard® comes in. The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization’s data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client’s data models – I will show you how to apply the Scorecard in this book. This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections: In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3. In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: · Chapter 4: Correctness · Chapter 5: Completeness · Chapter 6: Scheme · Chapter 7: Structure · Chapter 8: Abstraction · Chapter 9: Standards · Chapter 10: Readability · Chapter 11: Definitions · Chapter 12: Consistency · Chapter 13: Data In Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).