Джей Бортен | Jay Borthen | Data integration. Объединение данных для улучшения процесса принятия решений | Building Data Integration Solutions: Unifying Data for Enhanced Decision Making [2 книги] (2026) [PDF, EPUB, MOBI, AZW] [RU, EN]

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Предисловие
Благодарности
От издательства

ЧАСТЬ I ОСНОВЫ ИНТЕГРАЦИИ ДАННЫХ

Глава 1. Что такое интеграция данных
Глава 2. Основные концепции
Глава 3. Проблемы интеграции данных
Глава 4. Модели, архитектуры, методы и шаблоны

ЧАСТЬ II ИНСТРУМЕНТЫ, ТЕХНОЛОГИИ И ФРЕЙМВОРКИ

Глава 5. Какие инструменты для интеграции данных выбрать
Глава 6. Хранилища данных и системы управления
Глава 7. Инструменты загрузки и потоковой обработки данных
Глава 8. Комплексные пакеты интеграционных решений

ЧАСТЬ III ПРИМЕР РЕШЕНИЯ ДЛЯ ИНТЕГРАЦИИ ДАННЫХ

Глава 9. Вводная информация
Глава 10. Внедрение пакетного решения
Глава 11. Внедрение потокового решения

ПРИЛОЖЕНИЯ
Приложение А. Пример настройки решения для интеграции данных
Приложение Б. Список дополнительных источников

Глоссарий: ключевые термины
Глоссарий: аббревиатуры
Об авторе

Building Data Integration Solutions: Unifying Data for Enhanced Decision Making by Jay Borthen

In this essential book, Jay Borthen offers a comprehensive guide to understanding the art of data integration. This book dives deep into the processes and strategies necessary for creating effective data pipelines that ensure consistency, accuracy, and accessibility of your data. Whether you're a novice looking to understand the basics or an experienced professional aiming to refine your skills, Borthen's insights and practical advice, grounded in real-world case studies, will empower you to transform your organization's data handling capabilities.
This book presents a pragmatic, hands-on approach to data integration by first baselining the reader’s knowledge with important terminology and concepts and eventually walking through the building of a plausible, real-life data integration solution, step by step. For the hands-on parts in the later chapters of the book, familiarity with Linux, Python, Structured Query Language (SQL), and Amazon Web Services (AWS) would be beneficial, but I’ll attempt to explain what takes place at each step in simple terms.
It should be noted that containerization is not utilized in the hands-on sections of this book. However, it may be prudent for the practitioner to use containers (e.g., Docker) and perhaps even a distributed container management tool like Kubernetes for large, enterprise data integration initiatives. Also, while the book focuses on aspects directly related to data integration, because of inherent complexity within data engineering and data management, topic tangents and parallel subject matter discussions are inevitable. There are also many concepts included in the book that live within the blurred lines between data engineering and software development.
Supplemental material (code examples, exercises, etc.) is available for download at GitHub.

You will:
Understand various data integration solutions and how different technologies can be employed
Gain insights into the relationship between data integration and the overall data lifecycle
Learn to effectively design, set up, and manage data integration components within pipelines
Acquire the knowledge to configure pipelines, perform data migrations, transformations, and more

Contents:

Preface

I. Foundations of Data Integration
Introduction to Data Integration
Key Concepts in Data Integration
Data Integration Challenges
Models, Architectures, Methods, and Patterns

II. Tools, Technologies, and Frameworks
Data Integration Tool Options
Data Stores and Management Systems
Data Ingestion and Streaming Tools
Comprehensive Integration Suites

III. Introducing the Example Data Integration Solution
Introducing the Example Solution
Implementing a Batch Solution
mplementing a Streaming Solution
A. Setting Up the Data Integration Solution Example
B. References
Key Terms Glossary
Acronyms Glossary
Index