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Azure Data Engineering reveals the architectural, operational, and data management techniques that power cloud-based data infrastructure built on the Microsoft Azure platform. Author Vlad Riscuita, a data engineer at Microsoft, teaches you the patterns and techniques that support Microsoft’s own massive data infrastructure. You'll learn to bring an engineering rigor to your data platform, ensuring that your theoretical data tools function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, get to grips with DevOps for both analytics and machine learning, and more.A book that sucks you in at the start and doesn't want you to put it down until it has imparted all the knowledge you need to understand the topic.
About the Technology
There’s a big gap between running machine learning and data processes as prototypes, and deploying them to a production cloud environment. Robust data engineering practices are essential to ensuring that your carefully designed data tools have what it takes to work in the real world. Encompassing everything from architecture and design, to operations, monitoring, and scaling, a proper approach to data engineering ensures your systems are reliable and flexible enough to handle the different issues that messy data can throw at them.About the book
Azure Data Engineering teaches you to build a scalable and robust data platform to industry-leading standards. All examples are based on the production big data platform that powers Microsoft's customer-growth operations. You'll learn techniques and best practices that author Vlad Riscutia and his team use on a daily basis, including automation and DevOps, running a reliable machine learning pipeline, and managing your data inventory. Examples are illustrated with Azure. The patterns and techniques are transferable to other cloud platforms.placing your order...
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