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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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Going through the data exposition technologies

As we saw earlier, data needs vary widely, both in terms of user profiles and specific requirements. That’s why it’s essential to have tailored data exposition methods to meet each type of need. Depending on the size of the organization and the volume of the associated data, it may be beneficial to utilize one or multiple exposition systems.

Streams expositions

Data streaming is an advanced practice in data engineering that involves making data available in real time as it is generated.

This practice grants immediate access to data from various sources, such as sensors, social media platforms, and financial transactions.

The advantages of data streaming are particularly significant in fast-paced sectors such as finance, manufacturing, transportation, and logistics, where swift, informed decisions can influence competitiveness.

This chapter delves into the technology and applications associated with data streaming...

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