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Kia Persia Literary Agency
KIA Literary Agency was founded in 2002 in Tehran with the aim of promoting and supporting fine literary works in all forms throughout the world. It brings about opportunities for authors, illustrators, publishers, translators, and those involved in this field to meet their counterparts. And at the same time, it introduces them to the world and will inform them of all the related events which take place in the world of art and literature.
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Computer scienceAugust 2011Data Modeling Made Simple with CA ERwin Data Modeler r8
With Ca Erwin Data Modeler R8
by Donna Burbank, Steve Hoberman
Hear one of the authors, Steve Hoberman, talk about this book. 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: Understand the basics of data modeling and relational theory, and how to apply these skills using CA ERwin Data Modeler Read a data model of any size and complexity with the same confidence as reading a book 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 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 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 Share data model information with various audiences using model formatting and layout techniques, reporting, and metadata exchange Use the new workspace customization features in CA ERwin Data Modeler r8 to create a workflow suited to your own individual needs Leverage the new Bulk Editing features in CA ERwin Data Modeler r8 for mass metadata updates, as well as import/export with Microsoft Excel Compare and merge model changes using CA ERwin Data Modelers Complete Compare features Optimize the organization and layout of your data models through the use of Subject Areas, Diagrams, Display Themes, and more
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DatabasesOctober 2009Data Modeling Made Simple
A Practical Guide for Business and It Professionals
by Steve Hoberman
Hear the author, Steve Hoberman, talk about his book. Data Modeling Made Simple will provide the business or IT professional with a practical working knowledge of data modeling 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 normalized 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 modeling deliverables Learn basic UML concepts Put data modeling in context with XML, metadata, and agile development
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Computer scienceOctober 2011Data Modeling Made Simple with PowerDesigner
by Steve Hoberman, George McGeachie
Data Modeling Made Simple with PowerDesigner 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 PowerDesigner. Hear one of the authors, Steve Hoberman, talk about this book. You'll build many PowerDesigner data models along the way, increasing your skills first with the fundamentals and later with more advanced feature of PowerDesigner. This book combines real-world experience and best practices to help you master the following ten objectives: This book has ten key objectives for you, the reader: You will know when a data model is needed and which PowerDesigner 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 when to apply and how to make use of all the key features of PowerDesigner You will be able to build, step-by-step in PowerDesigner, a pyramid of linked data models, including a conceptual data model, a fully normalized relational data model, a physical data model, and an easily navigable dimensional model 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 governance and modeling consistency within your organization by leveraging features such as PowerDesigner’s reference models, Glossary, domains, and model comparison and model mapping techniques You will know how to utilize dependencies and traceability links to assess the impact of change You will know how to integrate your PowerDesigner models with externally-managed files, including the import and export of data using Excel and Requirements documents You will know where you can take advantage of the entire PowerDesigner model set, to increase the success rate of corporate-wide initiatives such as business intelligence and enterprise resource planning (ERP) You will understand the key differentiators between PowerDesigner and other data modeling tools you may have used before
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March 2013Data Modeling Made Simple with ER/Studio Data Architect
by Steve Hoberman
Hear the author, Steve Hoberman, talk about this book. Data Modeling Made Simple with ER/Studio Data Architect will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. You'll build many ER/Studio 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 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 You will be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio 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’s lineage functionality and compare/merge utility You will achieve enterprise architecture through ER/Studio’s repository and portal functionality You will be able to apply ER/Studio’s 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’s naming functionality to improve naming consistency
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Computer scienceApril 2009Data Modeling for the Business
A Handbook for Aligning the Business With It Using High-level Data Models
by Steve Hoberman, Donna Burbank, Chris Bradley
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. Hear one of the authors, Steve Hoberman, talk about this book. 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. Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book. As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general. This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT. One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements. Len Silverston, author of The Data Model Resource Book series
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Business & managementJune 2014Data Modeling for MongoDB
by Steve Hoberman
Learn how to capture and precisely document business requirements to create an efficient MongoDB design. Watch Steve Hoberman talk about his book. Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions. Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together. This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. About the Author: Steve Hoberman is the most requested data modeling instructor in the world. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then. Steve is the author of seven books on data modeling, the founder of the Design Challenges group, inventor of the Data Model Scorecard, Conference Chair of the Data Modeling Zone conference, and recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award. Steve can be reached at me@stevehoberman.com, @DataMdlRockStar on Twitter, or through Steve Hoberman on Linked-In.
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September 2012Data Modeling Master Class Training Manual 4th Edition
by Steve Hoberman
This is the fourth 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 course on requirements elicitation and data modeling, containing three days of practical techniques for producing solid relational and dimensional 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 also how to build a data model well. Two case studies and many exercises reinforce the material and enable you to apply these techniques in your current projects. By the end of the course, you will know how to… Explain data modeling building blocks and identify these constructs by following a question-driven approach to ensure model precision Demonstrate reading a data model of any size and complexity with the same confidence as reading a book Validate any data model with key "settings" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard Apply requirements elicitation techniques including interviewing and prototyping Build relational and dimensional conceptual, logical, and physical data models through two case studies Practice finding structural soundness issues and standards violations Recognize situations where abstraction would be most valuable and situations where abstraction would be most dangerous Use a series of templates for capturing and validating requirements, and for data profiling Express how to write clear, complete, and correct definitions Leverage the Grain Matrix, enterprise data model, and available industry data models for a successful enterprise architecture. Steve Hoberman is the most requested data modeling instructor in the world. Introduced at over 50 international conferences as everything from a "data modeling guru" to "data modeling rock star", he balances the formality and precision of data modeling with the realities of building software systems with severe time, budget, and people constraints. In his consulting and teaching, he focuses on templates, tools, and guidelines to reap the benefits of data modeling with minimal investment. He taught his first data modeling class in 1992 and has educated more than 10,000 people about data modeling and business intelligence techniques since then, spanning every continent except Africa and Antarctica. Steve is the recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, is the chair of the Data Modeling Zone conference, and is the author of five books on data modeling, including the bestseller Data Modeling Made Simple.
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Computer scienceJuly 2012The Analytical Puzzle
by David Haertzen
Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organization. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualization and mobile devices. Here are the main objectives for each of the book's 19 chapters: Chapter 1: Develop a foundational knowledge of data warehousing, business intelligence and analytics Chapter 2: Build the business case needed to sell your data warehousing project, and then produce a project plan that avoids common pitfalls Chapter 3: Elicit and organize business intelligence and data warehousing business requirements Chapter 4: Specify the technical architecture of the data warehousing system, including software and infrastructure components, technology stack, and non-functional requirements. Gain an understanding of cloud based data warehousing and data warehouse appliances Chapter 5: Learn about data attributes including metrics and key performance indicators (KPIs), the raw material of data warehousing and business intelligence Chapter 6: Learn about data modeling and how to apply design patterns for each part of the data warehouse Chapter 7: Speak the dimensional modeling language of measures, dimensions, facts, cubes, stars, and snowflakes Chapter 8: Organize a successful data governance program. Learn how to manage metadata for your data warehousing and business intelligence project Chapter 9: Identify useful data sources and implement a data quality program Chapter 10: Use database technology for your data warehousing project, and understand the impact of data warehouse appliances, big data, in memory databases, columnar databases and OnLine Analytical Processing (OLAP) Chapter 11: Apply data integration and understand the role data mapping, data cleansing, data transformation, and loading data play in a successful data warehouse Chapter 12: Use the business intelligence (BI) operations of slice, dice, drill down, roll up, and pivot to analyze and present data Chapter 13: Learn about descriptive and predictive statistics, and calculate mean, median, mode, variance and standard deviation Chapter 14: Harness analytical methods such as regression analysis, data mining, and statistics to make profitable decisions and anticipate the future Chapter 15: Appreciate the components and design patterns that compose a successful analytic application Chapter 16: Gain an understanding of the uses and benefits of scorecards and dashboards including support of mobile device users Chapter 17: Gain insight into applications of business intelligence that could profit your organization, including risk management, finance, marketing, government, healthcare, science and sports Chapter 18: Perform customer analytics to better understand and segment your customers Chapter 19: Test, roll out, and sustain the data warehouse David Haertzen is a seasoned data warehouse architect who has helped a diverse set of organizations from start-ups to multinationals to utilize data for their advantage. David teaches data modeling, data warehousing, data architecture, business intelligence and is also active as editor of the Infogoal.com Data Management Center. David is a graduate of the University of Minnesota and holds an MBA from the University of St. Thomas.
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Computer scienceNovember 2011UML and Data Modeling
A Reconciliation
by David C. Hay
Here you will learn how to develop an attractive, easily readable, conceptual, business-oriented entity/relationship model, using a variation on the UML Class Model notation. Hear the author, David Hay, talk about his book. This book has two audiences: Data modelers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don’t realize that architectural data modeling really is different from object modeling (and that the differences are important). David Hay’s objective is to finally bring these two groups together in peace. Here all modelers will receive guidance on how to produce a high quality (that is, readable) entity/relationship model to describe the data architecture of an organization. The notation involved happens to be the one for class models in the Unified Modeling Language, even though UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models, which means that to use UML for architectural modeling requires some adjustments. These adjustments are described in this book. David Hay is the author of Enterprise Model Patterns: Describing the World, a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction, and they were all rendered in the slightly modified version of UML Class Diagrams presented here. This book is a handbook to describe how to build models such as these. By way of background, an appendix provides a history of the two groups, revealing the sources of their different attitudes towards the system development process. If you are an old-school ER modeler and now find yourself having to come up to speed on UML to get that next job (or keep the current one), this is your guidebook to success. If you are a long time object oriented programmer who has to interact with data modelers, this book is for you too. David has done the hard work of mapping out how to do a logical entity relationship model using standard (and accepted) UML diagram components. This book shows you step-by-step, with ample examples, how to get from here to there with the least pain possible for all concerned. Kent Graziano Certified Data Vault Master and Oracle ACE Past-President of ODTUG & RMOUG
