Download or read book Data Modeling Made Simple with PowerDesigner written by Steve Hoberman and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Annotation This book will provide the business or IT professional with a practical working knowledge of data modelling concepts and best practices, and how to apply these principles with PowerDesigner. You will build many PowerDesigner data models along the way, increasing your skills in first the fundamentals and later in the book the more advanced features of PowerDesigner. The book contains six sections: Section I introduces data modelling along with its purpose and variations. Also included is an explanation of the important role of a data modelling tool, the key features required of any data modelling tool, and an introduction to the essential features of PowerDesigner; Section II explains all of the components on a data model including entities, data elements, relationships, and keys, and describes how to create and manage these objects in PowerDesigner. Also included is a discussion of the importance of quality names and definitions for your objects; Section III dives into the relational and dimensional subject area, logical, and physical data models, and describes how PowerDesigner supports these models and the connections between them. Learn how to get information into and out of PowerDesigner, and improve the quality of your data models with a cross-reference of key PowerDesigner features with the Data Model Scorecard; Section IV contains a PowerDesigner workshop designed to consolidate everything for you; Section V focuses on additional PowerDesigner features (some of which have already been introduced) which make life easier for data modellers; Section VI discusses PowerDesigner topics beyond data modelling, including the XML physical model and the other types of model available in PowerDesigner; it also discusses the role of PowerDesigner in data management, using the DAMA Data Management Body of Knowledge (DAMA-DMBOK) framework.
Download or read book Data Modeling Made Simple with Erwin DM written by Harris Jeff and published by . This book was released on 2020-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Modeling Made Simple with CA ERwin Data Modeler r8 written by Donna Burbank and published by Technics Publications. This book was released on 2011-08-01 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Modeling Made Simple with CA ERwin Data Modeler r8 will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You’ll build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. This book combines real-world experience and best practices with down to earth advice, humor, and even cartoons to help you master the following ten objectives: 1. Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler 2. Read a data model of any size and complexity with the same confidence as reading a book 3. Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin’s Data Modelers Design Layer Architecture 4. Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both ‘top down’ and bottom-up design 5. Learn how to create reusable domains, naming standards, UDPs, and model templates in CA ERwin Data Modeler to reduce modeling time, improve data quality, and increase enterprise consistency 6. Share data model information with various audiences using model formatting and layout techniques, reporting, and metadata exchange 7. Use the new workspace customization features in CA ERwin Data Modeler r8 to create a workflow suited to your own individual needs 8. Leverage the new Bulk Editing features in CA ERwin Data Modeler r8 for mass metadata updates, as well as import/export with Microsoft Excel 9. Compare and merge model changes using CA ERwin Data Modelers Complete Compare features 10. Optimize the organization and layout of your data models through the use of Subject Areas, Diagrams, Display Themes, and more Section I provides an overview of data modeling: what it is, and why it is needed. The basic features of CA ERwin Data Modeler are introduced with a simple, easy-to-follow example. Section II introduces the basic building blocks of a data model, including entities, relationships, keys, and more. How-to examples using CA ERwin Data Modeler are provided for each of these building blocks, as well as ‘real world’ scenarios for context. Section III covers the creation of reusable standards, and their importance in the organization. From standard data modeling constructs such as domains to CA ERwin-specific features such as UDPs, this section covers step-by-step examples of how to create these standards in CA ERwin Data Modeling, from creation, to template building, to sharing standards with end users through reporting and queries. Section IV discusses conceptual, logical, and physical data models, and provides a comprehensive case study using CA ERwin Data Modeler to show the interrelationships between these models using CA ERwin’s Design Layer Architecture. Real world examples are provided from requirements gathering, to working with business sponsors, to the hands-on nitty-gritty details of building conceptual, logical, and physical data models with CA ERwin Data Modeler r8. From the Foreword by Tom Bilcze, President, CA Technologies Modeling Global User Community: Data Modeling Made Simple with CA ERwin Data Modeler r8 is an excellent resource for the ERwin community. The data modeling community is a diverse collection of data professionals with many perspectives of data modeling and different levels of skill and experience. Steve Hoberman and Donna Burbank guide newbie modelers through the basics of data modeling and CA ERwin r8. Through the liberal use of illustrations, the inexperienced data modeler is graphically walked through the components of data models and how to create them in CA ERwin r8. As an experienced data modeler, Steve and Donna give me a handbook for effectively using the new and enhanced features of this release to bring my art form to life. The book delves into advanced modeling topics and techniques by continuing the liberal use of illustrations. It speaks to the importance of a defined data modeling architecture with soundly modeled data to assist the enterprise in understanding of the value of data. It guides me in applying the finishing touches to my data designs.
Download or read book Data Modeling Made Simple written by Steve Hoberman and published by Technics Publications, LLC. This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices, along with how to apply these principles with ER/Studio DA.You will build many ER/Studio DA data models along the way, applying best practices to master these ten objectives: You will know why a data model is needed and which ER/Studio DA models are the most appropriate for each situation; You will be able to read a data model of any size and complexity with the same confidence as reading a book; You will know how to apply all the key features of ER/Studio DA; You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio DA; 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; You will improve data model quality and impact analysis results by leveraging ER/Studio DAs lineage functionality and compare/merge utility; You will achieve enterprise architecture through ER/Studio DAs repository and portal functionality; You will be able to apply ER/Studio DAs data dictionary features; You will learn ways of sharing the data model through reporting and through exporting the model in a variety of formats; You will leverage ER/Studio DAs naming functionality to improve naming consistency.This book contains four sections: Section I introduces data modelling and the ER/Studio DA landscape. Learn why data modelling is so critical to software development and even more importantly, why data modelling is so critical to understanding the business. You will also learn about the ER/Studio DA environment. By the end of this section, you will have created and saved your first data model in ER/Studio DA and be ready to start modelling 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 DA. 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 DA. Section IV discusses additional features of ER/Studio DA. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.
Download or read book Data Modeling Made Simple written by Steve Hoberman and published by Technics Publications, LLC. This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ever have a bad data day? If you are a business user, architect, analyst, designer or developer, then you have 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 actual data. That's where a data model comes in handy. It's a diagram that uses text and symbols to represent groupings of data, giving you a clear picture of your business and application environment. The book provides the tools you need to read, create and validate models of your business and applications. Contains everything about modelling you need to know but were too afraid to ask, such as: What are the traditional and non-traditional uses of a data model? How do subject area, logical, and physical data models differ? When do I build a BSAM, ASAM, or CSAM? What is the easiest way to apply normalisation? Where can I best leverage abstraction? How do I decide whether to use denormalisation or dimensionality? What are primary, foreign, alternate, virtual, and surrogate keys? What is the best approach to building the models? How can I use the Scorecard system to validate a data model? Includes over 30 exercises to reinforce concepts and sharpen your skills!
Download or read book Data Modeling Made Simple written by Steve Hoberman and published by Technics Publications Llc. This book was released on 2009 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Read today's business headlines and you will see that many issues stem from people not having the right data at the right time. Data issues don't always make the front page, yet they exist within every organisation. We need to improve how we manage data -- and the most valuable tool for explaining, vaildating and managing data is a data model. This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation; Read a data model of any size and complexity with the same confidence as reading a book; Build a fully normalised relational data model, as well as an easily navigatable dimensional model; Apply techniques to turn a logical data model into an efficient physical design; Leverage several templates to make requirements gathering more efficient and accurate; Explain all ten categories of the Data Model Scorecard®; Learn strategies to improve your working relationships with others; Appreciate the impact unstructured data has, and will have, on our data modelling deliverables; Learn basic UML concepts; Put data modelling in context with XML, metadata, and agile development.
Download or read book Data Modeling Made Simple with Embarcadero ER Studio Data Architect written by Steve Hoberman and published by . This book was released on 2015-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Resource Data written by Michael H.. Brackett and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Model Scorecard written by Steve Hoberman and published by Technics Publications. This book was released on 2015-11-01 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Download or read book Expert Data Modeling with Power BI written by Soheil Bakhshi and published by Packt Publishing Ltd. This book was released on 2021-06-11 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Manage and work with business data effectively by learning data modeling techniques and leveraging the latest features of Power BI Key Features Understand data modeling techniques to get the best out of data using Power BI Define the relationships between data to extract valuable insights Solve a wide variety of business challenges by building optimal data models Book DescriptionThis book is a comprehensive guide to understanding the ins and outs of data modeling and how to create data models using Power BI confidently. You'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. In this book, you'll explore how to use data modeling and navigation techniques to define relationships and create a data model before defining new metrics and performing custom calculations using modeling features. As you advance through the chapters, the book will demonstrate how to create full-fledged data models, enabling you to create efficient data models and simpler DAX code with new data modeling features. With the help of examples, you'll discover how you can solve business challenges by building optimal data models and changing your existing data models to meet evolving business requirements. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support reporting and data analytics.What you will learn Implement virtual tables and time intelligence functionalities in DAX to build a powerful model Identify Dimension and Fact tables and implement them in Power Query Editor Deal with advanced data preparation scenarios while building Star Schema Explore best practices for data preparation and modeling Discover different hierarchies and their common pitfalls Understand complex data models and how to decrease the level of model complexity with different approaches Learn advanced data modeling techniques such as aggregations, incremental refresh, and RLS/OLS Who this book is for This MS Power BI book is for BI users, data analysts, and analysis developers who want to become well-versed with data modeling techniques to make the most of Power BI. You’ll need a solid grasp on basic use cases and functionalities of Power BI and Star Schema functionality before you can dive in.
Download or read book Model Based Environment written by Vladimir Pantic and published by Trafford Publishing. This book was released on 2013-02-14 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information Systems are a synthesis of complex components where data plays a critical role. Data Modeling requires a disciplined approach making use of business and technical knowledge. Using data models for database design, implementation, and maintenance requires the implementation of procedures that will secure successful database deployment and validation. This book teaches you the basic technical knowledge required for physical data modeling as well as procedures for model implementation and maintenance. With examples in two major Relational Database Management Systems (Oracle and DB2) the book presents procedures for model design, implementation and maintenance in PowerDesigner modeling tool.
Download or read book Expert Data Modeling with Power BI written by Soheil Bakhshi and published by Packt Publishing Ltd. This book was released on 2023-04-28 with total page 699 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your Power BI reports to the next level by learning various data modeling techniques and leveraging the latest features of Power BI effectively Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Get an understanding of data modeling techniques using Power BI with this up-to-date guide Learn how to define the relationships between data sets to extract valuable insights Explore best practices for data preparation and modeling and build optimal data models to solve a wide variety of real-world business challenges Book Description This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create full-fledged data models using Power BI confidently. In this new, fully updated edition, you'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. As you advance through the chapters, the book will demonstrate how to prepare efficient data models in the Power Query Editor and use simpler DAX code with new data modeling features. You'll explore how to use the various data modeling and navigation techniques and perform custom calculations using the modeling features with the help of real-world examples. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. Additionally, you'll learn valuable best practices and explore common data modeling complications and the solutions to supercharge the process of creating a data model in Power BI and build better-performing data models. By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support high-performing reports and data analytics. What you will learn Implement virtual tables and time intelligence functionalities in DAX to build a powerful model Identify Dimension and Fact tables and implement them in Power Query Editor Deal with advanced data preparation scenarios while building Star Schema Discover different hierarchies and their common pitfalls Understand complex data models and how to decrease the level of model complexity with different approaches Learn advanced data modeling techniques such as calculation groups, aggregations, incremental refresh, RLS/OLS, and more Get well-versed with datamarts and dataflows in PowerBI Who this book is for This MS Power BI book is for BI users, data analysts, and analysis developers who want to become well-versed with data modeling techniques to make the most of Power BI. Basic working knowledge of Power BI and the Star Schema functionality are required to help you to understand the concepts covered in this book.
Download or read book Data Modeling Master Class Training Manual written by Steve Hoberman and published by . This book was released on 2015-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Database Modeling and Design written by Toby J. Teorey and published by Elsevier. This book was released on 2011-02-10 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Database Modeling and Design, Fifth Edition, focuses on techniques for database design in relational database systems. This extensively revised fifth edition features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to any SQL-based system. The common examples are based on real-life experiences and have been thoroughly class-tested. This book is immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data. It is ideal for a stand-alone data management course focused on logical database design, or a supplement to an introductory text for introductory database management. In-depth detail and plenty of real-world, practical examples throughout Loaded with design rules and illustrative case studies that are applicable to any SQL, UML, or XML-based system Immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data
Download or read book Data Modeling Master Class Training Manual written by Steve Hoberman and published by . This book was released on 2019-05-14 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Modeling Master Class Training Manual 7th Edition written by Steve Hoberman and published by . This book was released on 2017-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Data Modeling A Beginner s Guide written by Andy Oppel and published by McGraw Hill Professional. This book was released on 2009-11-23 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Skills--Made Easy! Learn how to create data models that allow complex data to be analyzed, manipulated, extracted, and reported upon accurately. Data Modeling: A Beginner's Guide teaches you techniques for gathering business requirements and using them to produce conceptual, logical, and physical database designs. You'll get details on Unified Modeling Language (UML), normalization, incorporating business rules, handling temporal data, and analytical database design. The methods presented in this fast-paced tutorial are applicable to any database management system, regardless of vendor. Designed for Easy Learning Key Skills & Concepts--Chapter-opening lists of specific skills covered in the chapter Ask the expert--Q&A sections filled with bonus information and helpful tips Try This--Hands-on exercises that show you how to apply your skills Notes--Extra information related to the topic being covered Self Tests--Chapter-ending quizzes to test your knowledge Andy Oppel has taught database technology for the University of California Extension for more than 25 years. He is the author of Databases Demystified, SQL Demystified, and Databases: A Beginner's Guide, and the co-author of SQL: A Beginner's Guide, Third Edition, and SQL: The Complete Reference, Third Edition.
