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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 storage and modeling techniques

The act of developing a visual representation of an organization’s data and its relationships is known as data modeling. This representation, or model, assists developers and data architects in designing databases and systems that fit the needs of the organization. In data architecture, several data modeling strategies are routinely employed, and selecting the proper one might be important to the success of your analytics project. In this section, we will go through several data modeling strategies, their benefits and drawbacks, and how they can be used in various contexts.

Normalization and denormalization

Before diving into various modeling techniques, it is crucial to grasp the concepts of normalization and denormalization since they provide the foundation for understanding the entity-relationship model (ERM) and the star schema.

Normalization is a critical practice in database design that aims to eliminate redundancy and enhance...

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