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The Definitive Guide to Data Integration

The Definitive Guide to Data Integration

By : BONNEFOY, CHAIZE, Raphaël MANSUY, Mehdi TAZI
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The Definitive Guide to Data Integration

The Definitive Guide to Data Integration

By: BONNEFOY, CHAIZE, Raphaël MANSUY, Mehdi TAZI

Overview of this book

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.
Table of Contents (19 chapters)
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Summary

This chapter provided an extensive overview of various data integration techniques, aiming to equip you with an understanding of diverse models, architectures, and patterns employed in data integration. This chapter began by comparing point-to-point integration and middleware-based integration, elaborating on their respective advantages, disadvantages, and use cases. Then, we transitioned to a comprehensive review of data integration architectures while discussing the mechanics, trade-offs, and applicability of batch, micro-batching, real-time, and incremental data integration. Next, we explored popular data integration patterns, including ETL and ELT, along with several others, such as CDC and data federation. Finally, we covered data integration organizational models, providing a deep dive into the traditional (monolithic) architecture, the data mesh model, and the data lake architecture. We concluded by offering guidance on choosing the right integration model, with a detailed...

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