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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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Massively parallel processing

Massively parallel processing (MPP) engines and databases represent a distinct type of data processing system, specifically designed to execute complex analytical queries over large data volumes. They employ a divide-and-conquer methodology, enabling speedy data processing by distributing data across multiple nodes for simultaneous processing.

At their core, MPP engines harness the power of parallelism. Each node operates independently of the others, working on its portion of the data. Every node has storage, memory, and processing units, ensuring that there is no bottleneck in terms of resources. This distribution of data and computation is what grants MPP engines and databases their scalability and efficiency. As data grows or the demand for faster insights increases, additional nodes can be incorporated into the system seamlessly.

What sets MPP engines and databases apart from traditional databases is their architectural design. They use a shared...

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