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.

About the Author

Vlad Riscutia is a software architect and data engineer at Microsoft on the Customer Growth and Analytics team.

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