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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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Why, when, and where to perform data preparation

In the dynamic world of data management and analysis, the preparation of data holds a pivotal role. This section aims to shed light on the key components and concepts of data preparation. It also underscores its significance in the broader framework of data integration.

Data preparation is often likened to setting the stage before a grand performance. It’s the process of cleaning, structuring, and enriching raw data to improve its quality and usefulness. Similar to a well-prepared stage enhancing an artist’s performance, well-prepared data can significantly improve the accuracy and reliability of data analysis, thereby leading to more insightful and actionable business intelligence.

First and foremost, let’s delve into the essence of data preparation. At its core, it’s about making raw data more suitable for analytics or other business processes. This involves various sub-processes, including data cleansing...

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