Learn the essentials of data integration with this comprehensive guide, covering everything from sources to solutions, and discover the key to making the most of your data stack

Key Features

• Learn how to leverage modern data stack tools and technologies for effective data integration
• Design and implement data integration solutions with practical advice and best practices
• Focus on modern technologies such as cloud-based architectures, real-time data processing, and open-source tools and technologies

Book Description

The Definitive Guide to Data Integration is an indispensable resource for navigating the complexities of modern data integration. Focusing on the latest tools, techniques, and best practices, this guide helps you master data integration and unleash the full potential of your data. This comprehensive guide begins by examining the challenges and key concepts of data integration, such as managing huge volumes of data and dealing with the different data types. You'll gain a deep understanding of the modern data stack and its architecture, as well as the pivotal role of open-source technologies in shaping the data landscape. Delving into the layers of the modern data stack, you'll cover data sources, types, storage, integration techniques, transformation, and processing. The book also offers insights into data exposition and APIs, ingestion and storage strategies, data preparation and analysis, workflow management, monitoring, data quality, and governance. Packed with practical use cases, real-world examples, and a glimpse into the future of data integration, The Definitive Guide to Data Integration is an essential resource for data eclectics. By the end of this book, you'll have the gained the knowledge and skills needed to optimize your data usage and excel in the ever-evolving world of data.

What you will learn

• Discover the evolving architecture and technologies shaping data integration
• Process large data volumes efficiently with data warehousing
• Tackle the complexities of integrating large datasets from diverse sources
• Harness the power of data warehousing for efficient data storage and processing
• Design and optimize effective data integration solutions
• Explore data governance principles and compliance requirements

Who this book is for

This book is perfect for data engineers, data architects, data analysts, and IT professionals looking to gain a comprehensive understanding of data integration in the modern era. Whether you're a beginner or an experienced professional enhancing your knowledge of the modern data stack, this definitive guide will help you navigate the data integration landscape.

Table of Contents

1. Introduction to our data integration journey
2. Introducing Data Integration
3. Architecture and History of Data Integration
4. Data Sources and Types
5. Columnar Data Formats and comparisons
6. Data Storage Technologies and Architectures
7. Data Ingestion and Storage Strategies
8. Data Integration Techniques
9. Data Transformation and Processing
10. Transformation patterns, cleansing and normalization
11. Data Exposition and APIs
12. Data Preparation and Analysis
13. Workflow Management, Monitoring, and Data Quality
14. Lineage, Governance, and Compliance
15. Various Architecture Use Cases
16. Prospects and Challenges

Review

“Authored by seasoned data professionals with extensive project deployments, this book offers a comprehensive overview of data integration. Covering essential concepts, techniques, and tools, this book is a compass for every data professional seeking to create value and transform their business.”

Stephane Heckel, Data Sommelier. DATANOSCO

About the Author

Pierre-Yves Bonnefoy is a versatile Data & Cloud Architect boasting over 20 years of experience across diverse technical and functional domains. With an extensive background in software development, systems and networks, data analytics, and data science, Pierre-Yves offers a comprehensive view of information systems. As the CEO of Olexya and CTO of Africa4Data, he dedicates his efforts to delivering cutting-edge solutions for clients and promoting data-driven decision making. As an active board member of French Tech Le Mans, Pierre-Yves enthusiastically supports the local tech ecosystem, fostering entrepreneurship and innovation while sharing his expertise with the next generation of tech leaders.

Emeric Chaize, with over 16 years of experience in data management and cloud technology, demonstrates profound knowledge of data platforms and their architecture, further exemplified by his role as President of Olexya, a Data Architecture company. His background in Computer Science and Engineering, combined with hands-on experience, has honed his skills in understanding complex data architectures and implementing efficient data integration solutions. His work at various small and large companies has demonstrated his proficiency in implementing cloud-based data platforms and overseeing data-driven projects, making him highly suited for roles involving data platforms and data integration challenges.

Raphaël Mansuy is a seasoned technology executive and entrepreneur with over 25 years of experience in software development, digital transformation, and AI-driven solutions. As a founder of several companies, he has demonstrated success in designing and implementing mission-critical solutions for global enterprises, creating innovative technologies, and fostering business growth. Raphaël is highly skilled in AI, data engineering, DevOps, and cloud-native development, offering consultancy services to Fortune 500 companies and startups alike. He is passionate about enabling businesses to thrive using cutting-edge technologies and insights.

Mehdi TAZI is a Data & Cloud Architect with over 12 years of experience and the CEO of an IT consulting & Investment companies. He is specialized in distributed information systems and Data Architecture. Mehdi designs Information Systems Architectures that answer customers' needs by setting up technical, functional, and organizational solutions, as well as designing and coding in programming languages such as Java, Scala, or Python.