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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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Understanding data quality and data observability

Data quality and data observability are two vital and interconnected facets of robust data management strategies. Ensuring data quality involves various techniques and methodologies to enhance the accuracy, consistency, completeness, and relevance of data. On the other hand, data observability involves providing visibility into the data pipeline, allowing organizations to trace data’s journey, detect anomalies, and ensure its reliability. Together, these elements help enterprises build a strong data foundation, leading to more reliable insights and informed decision-making.

Introduction to data quality and observability

Data quality and observability form the backbone of reliable and insightful data analytics. When we speak of data quality, we are referring to the degree of excellence exhibited in the data content using various dimensions, such as accuracy, consistency, completeness, and reliability. Data observability...

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