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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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Data Preparation and Analysis

Preparing and analyzing data is an important part of data science and analytics. As data sets grow in size and complexity, effective data preparation and analysis are more crucial than ever before for making informed decisions and gaining useful insights. This chapter goes into the procedures, tactics, and governing factors of carrying out such endeavors.

In the first part of the chapter, we break down what data preparation is, when it’s needed, and where it’s done. Data quality, scalability, reporting needs, data storage, governance, and cooperation are just a few of the things that affect when and where data preparation takes place.

Going further, we will delve into data transformation, discussing methods, options, and consequences. We’ll discuss how to create a plan for transforming data, how to choose the best data transformations, and how to put that plan into action while maximizing its effectiveness. You’ll get...

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