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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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Summary

This chapter examined various data transformation methodologies, tools, and use cases, including filters, aggregations, and join. Each operation’s utility and function were stated, which enabled us to cover practical applications.

Next, we explored data transformation use cases in sales analysis, social media analysis, customer segmentation, and website analytics. These case studies demonstrate the concepts’ efficacy.

SQL and Spark, two key data transformation tools, dominated this chapter. SQL, a popular query language, is used to change data, whereas Spark is a powerful data processing engine. We compared SQL and Spark’s DataFrame API to show these tools’ adaptability.

Finally, we discussed the main data transformation techniques, which include event, batch, and stream processing. We emphasized their unique features and usefulness before covering windowing. After, you learned about data transformations through practical examples and were...

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