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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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Central analytics data storage technologies

To help you grasp the differences between different storage systems, first, we will go over the evolution of data storage options. In the early days of computing, data storage was limited to tangible media such as tapes and hard drives. As businesses expanded and data volumes grew, the need for more efficient and scalable storage solutions became evident. This resulted in the creation of relational databases, which enabled structured storing of data, as well as the capacity to query it using SQL.

However, as the variety and volume of data increased at an exponential rate, corporations confronted new issues in data storage and processing. The rise of big data, as defined by the three Vsvolume, variety, and velocity – necessitated the development of new storage solutions capable of handling the massive volumes of data generated by modern enterprises. As a result, data warehouses, data lakes, and object storage emerged as...

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