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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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Optimizing storage performance

As we continue our discussion of data modeling techniques, we will look at some advanced techniques that can help you further optimize your data architecture for analytics and reporting. Partitioning, bucketing, and Z-ordering are all techniques that can improve query performance and data organization in your system.

Indexing

Indexing is a strategy for improving database query performance by establishing and maintaining a data structure (an index) that allows for faster data retrieval. Indexes on one or more columns of a table can be defined and can improve query performance. Indexes, on the other hand, have a cost in that they demand additional storage and might slow down data modification activities such as inserts, updates, and deletes. As a result, striking a balance between generating indexes for query efficiency and controlling the related overhead is critical.

Partitioning

Unlike indexing, partitioning does not require additional costs...

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