Data modeling master class training manual 9781634621946

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Автор: Hoberman, Steve (Стив Хоберман)
Название: Data modeling master class training manual
Перевод названия: Стив Хоберман: Учебное пособие по моделированию данных. Мастер-класс
ISBN: 9781634621946
Издательство: Gazelle Book Services
Классификация:

Сбор и анализ данных

ISBN-10: 1634621948
Обложка/Формат: Paperback
Страницы: 346
Вес: 0.81 кг.
Дата издания: 15.07.2017
Серия: Computing & IT
Язык: English
Размер: 220 x 280 x 27
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Database design & theory
Подзаголовок: Steve hoberman`s best practices approach to understanding & applying fundamentals through advanced modeling techniques
Рейтинг:
Поставляется из: Англии
Описание: 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 Hobermans 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 modelling 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, artefact 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. Recognise 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, Hoberman, Steve