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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 integration architectures

At first, the need for distinct data architectures didn’t seem crucial. However, over time, specialized and centralized architectures started to take shape. As needs diversified and grew, the demand for decentralized or microservice architectures also emerged, highlighting new requirements that had previously been overlooked.

Traditional data warehouses and ETL processes

Data warehouses have been at the core of data integration for decades. Traditional data warehouses are centralized repositories that are designed to store and manage large volumes of structured data from various sources. They enable organizations to consolidate their data, perform analytical queries, and generate valuable insights for informed decision-making.

One of the key aspects of traditional data warehouses is the ETL process. This process involves three main steps:

  • Extract: Data is extracted from various sources, such as relational databases, flat files...

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