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Build a data platform to the industry-leading standards set by Microsoft''s own infrastructure.
In Data Engineering on Azure you will learn how to:
Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft''s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You''ll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning.
There''s a big gap between running machine learning and data processes as prototypes, and deploying them to a production cloud environment. Data Engineering on Azure reveals the architectural, operational, and data management techniques that power cloud-based data infrastructure built on the Microsoft Azure platform.
Data Engineering on Azure 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.
About the Technology
Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify.
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