A goldmine of valuable tools for data modelers! Data modelers render raw data-names, addresses, and sales totals, for instance-into information such as customer profiles and seasonal buying patterns that can be used for making critical business decisions. This book brings together thirty of the most effective tools for solving common modeling problems. The author provides an example of each tool and describes what it is, why it is needed, and how it is generally used to model data for both databases and data warehouses, along with tips and warnings. Blank sample copies of all worksheets and checklists described are provided in an appendix. Companion Web site features updates on the latest tools and techniques, plus links to related sites offering automated tools.

Data Modeler's Workbench w/WS

Computers & Technology

Hoberman, Steve

Networking & Cloud Computing

John Wiley&Sons

Ever have a bad data day? If you're a business user, architect, analyst, designer or developer, then you've probably had some bad data days. It comes with the territory. Overcoming these problems is much easier if you have an in-depth understanding of the

Data Modeling Made Simple: A Practical Guide for Business & IT Professionals

Business & Money

Technics Pubns Llc

This is 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.
 The Master Class is a complete course on data modeling, containing four days of practical techniques for producing solid relational and dimensional data models. After learning modeling concepts and terms, you will apply a best practices approach to building and validating data models through the Data Model Scorecard™. You will learn not just how to build a data model, but also how to build a data model well. Challenging exercises and workshops will reinforce the material and enable you to apply these techniques in your current projects. 
 Steve is an excellent presenter, keeping his audience engaged while covering in-depth relational and dimensional modeling techniques. His entertaining style combined with his many real world examples and exercises, held our interest throughout the course. S. DeCandia, Pfizer Global Manufacturing 
 In my long professional career, I have participated in many training seminars but I have never encountered a class in which the subject had been so thoroughly considered and presented in such a clear and engaging manor. As a fairly new, but full time data modeler, I expect to use the things that I learned in this class every day. My only regret is that the class had to end. G. Schmid, Travelers Insurance
 This was the most comprehensive, informative, energetic, interesting and just plain FUN class I have ever taken on the subject of Data Modeling. G. Werner, Long Island Railroad
 Part 1: Modeling Basics Assuming no prior knowledge of data modeling, we will begin this section with an entertaining exercise that will illustrate an important gap filled by data models. Next, we will explain data modeling concepts and terminology. We will also explore each component on a data model and practice reading business rules. 
 Part 2: Overview to the Data Model Scorecard™ The Scorecard is a set of ten categories for validating a data model. We will explore best practices from the perspectives of both the modeler and reviewer, and you will be provided with a template to use on your current projects. 
 Part 3: Understanding subject area, logical, and physical data models The subject area model captures a business need within a well-defined scope; the logical data model captures an application-independent business solution; and the physical data model captures the technical solution by focusing on factors such as performance and security. 
 Part 4: Ensuring the model captures the requirements We will focus on techniques such as the use of spreadsheets and business assertions to ensure the data model meets the business requirements. 
 Part 5: Validating model scope We will focus on techniques for validating that the scope of the requirements matches the scope of the model. If the scope of the model is greater than the requirements, we have a situation known as scope creep. If the model scope is less than the requirements, we will be leaving information out of the resulting application. 
 Other training modules include: <ul>
<li>Following acceptable modeling principles </li>
<li>Determining the optimal use of generic concepts </li>
<li>Applying consistent naming standards </li>
<li>Arranging the model for maximum understanding </li>
<li>Writing clear, correct, and consistent definitions </li>
<li>Matching the model with the enterprise </li>
<li>Comparing the meta data with the data </li>
</ul>

Data Modeling Master Class Training Manual (Take It With You)

Databases & Big Data

Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization. This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach.

Data Modeling for the Business: A Handbook for Aligning the Business with IT using High-Level Data Models (Take It with You Guides)

Burbank, Donna

Bradley, Chris

Technics Publications, LLC

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: 
<ul>
<li>Chapter 4: Correctness </li>
<li>Chapter 5: Completeness </li>
<li>Chapter 6: Scheme </li>
<li>Chapter 7: Structure </li>
<li>Chapter 8: Abstraction </li>
<li>Chapter 9: Standards </li>
<li>Chapter 10: Readability </li>
<li>Chapter 11: Definitions </li>
<li>Chapter 12: Consistency </li>
<li>Chapter 13: Data</li>
</ul> 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).

Data Model Scorecard: Applying the Industry Standard on Data Model Quality

Technics Publications

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:
<ol>
<li>Explain data modeling components and identify them on your projects by following a question-driven approach </li> <li>Demonstrate reading a data model of any size and complexity with the same confidence as reading a book </li> <li>Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard</li> <li>Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing </li> <li>Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions </li> <li>Practice finding structural soundness issues and standards violations </li> <li>Recognize when to use abstraction and where patterns and industry data models can give us a great head start </li> <li>Use a series of templates for capturing and validating requirements, and for data profiling </li> <li>Evaluate definitions for clarity, completeness, and correctness </li> <li>Leverage the Data Vault and enterprise data model for a successful enterprise architecture.</li> </ol>

Data Modeling Master Class Training Manual 6th Edition: Steve Hoberman’s Best Practices Approach to Developing a Competency in Data Modeling

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: 
<ol>
<li>Know why a data model is needed and which ER/Studio models are the most appropriate for each situation </li>
<li>Understand each component on the data model and how to represent and create them in ER/Studio </li>
<li>Know how to leverage ER/Studio's latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases </li>
<li>Know how to apply all the foundational features of ER/Studio </li>
<li>Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio </li>
<li>Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design </li>
<li>Improve data model quality and impact analysis results by leveraging ER/Studio's lineage functionality and compare/merge utility </li>
<li>Be able to apply ER/Studio's data dictionary features </li>
<li>Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats </li>
<li>Leverage ER/Studio's naming functionality to improve naming consistency, including the new Automatic Naming Translation feature. </li>
</ol>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.

Data Modeling Made Simple with Embarcadero ER/Studio Data Architect: Adapting to Agile Data Modeling in a Big Data World

Computer Science

Learn how blockchain works, where to use it within your organization, and how it will impact data management. This book contains three parts: <ol>
<li>
Explanation. Part I will explain will explain the concepts underlying blockchain. A precise and concise definition is provided, distinguishing blockchain from blockchain architecture. Variations of blockchain are explored based upon the concepts of purpose and scope. </li>
<li>
Usage. Now that you understand blockchain, where do you use it? The reason for building a blockchain application must include at least one of these five drivers: transparency, streamlining, privacy, permanence, or distribution. Usages based upon these five drivers are shown for finance, insurance, government, manufacturing and retail, utilities, healthcare, nonprofit, and media. Process diagrams will illustrate each usage through inputs, guides, enablers, and outputs. Also examined are the risks of applying these usages, such as cooperation, incentives, and change. </li>
<li>
Impact. Now that you know where to use blockchain, how will it impact our existing IT (Information Technology) environment? Part III explores how blockchain will impact data management. The Data Management Body of Knowledge 2nd Edition (DAMA-DMBOK2) is an amazing book that defines the data management field along with the often complex relationships that exist between the various data management disciplines. Learn how blockchain will impact each of these 11 disciplines: Data Governance, Data Architecture, Data Modeling and Design, Data Storage and Operations, Data Security, Data Integration and Interoperability, Document and Content Management, Reference and Master Data, Data Warehousing and Business Intelligence, Metadata Management, and Data Quality Management. </li>
</ol> Once you understand blockchain concepts and principles, you can position yourself, department, and organization to leverage distributed ledger technology.

Blockchainopoly: How Blockchain Changes the Rules of the Game

Economics

Each book in the Align > Refine > Design series covers conceptual, logical, and physical data modeling (schema design) for a specific database product, combining the best of data modeling practices with solution-specific considerations, which lead to effective communication tools and extensible datastore foundations. Read Neo4j Data Modeling if you are a data architect or modeler who needs to expand your modeling skills to include Neo4j, or if you are a database administrator or developer who knows Neo4j but needs to expand your schema design skills. The book's introduction and three chapters cover the proven approach. The introduction covers the three modeling characteristics of precise, minimal, and visual; the three model components of entities, relationships, and attributes; the three model levels of conceptual (align), logical (refine), and physical (design); the three modeling perspectives of relational, dimensional, and NoSQL; and the three modeling challenges with NoSQL (tactical, strategy, and cultural). Next, Chapter 1 covers Align, Chapter 2 Refine, and Chapter 3 Design. An animal shelter case study creates continuity across these three modeling levels. If you are interested in learning how to build multiple database solutions, read all the books in the Align > Refine > Design series. Since each book is created from the same template, once you read one, you'll be able to pick up the techniques for another database solution quickly.

Neo4j Data Modeling

Fauth, David

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. 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:<ol>
<li>Explain data modeling components and identify them on your projects by following a question-driven approach </li> <li>Demonstrate reading a data model of any size and complexity with the same confidence as reading a book </li> <li>Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard</li> <li>Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing </li> <li>Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions </li> <li>Practice finding structural soundness issues and standards violations </li> <li>Recognize when to use abstraction and where patterns and industry data models can give us a great head start </li> <li>Use a series of templates for capturing and validating requirements, and for data profiling </li> <li>Evaluate definitions for clarity, completeness, and correctness </li> <li>Leverage the Data Vault and enterprise data model for a successful enterprise architecture.</li> </ol>

Data Modeling Master Class Training Manual 7th Edition: Steve Hoberman's Best Practices Approach to Understanding and Applying Fundamentals Through Advanced Modeling Techniques

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