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Data Engineering on Azure

Vlad Riscutia
  • MEAP began October 2020
  • Publication in July 2021 (estimated)
  • ISBN 9781617298929
  • 375 pages (estimated)
  • printed in black & white

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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.

Richard Vaughan
Look inside
Data Engineering on Azure 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.

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

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.

what's inside

  • Pick the right Azure services for different data scenarios
  • Implement production quality data modeling, analytics, and machine learning workloads
  • Handle data governance
  • Apply best practices for compliance and access control

about the reader

For data engineers familiar with cloud and DevOps.

about the author

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

FREE domestic shipping on orders of three or more print books

If you are looking for a unified approach to integrate multiple/large heterogeneous data sources, you will find it in the book.

Glenn Leo Swonk

If you want to learnabout data engineering in general and the Azure platform specifically, this is a great place to start.

Peter Morgan

Well written with excellent graphics to explain the different components that make up Azure.

Vishwesh Ravi Shrimali

If you are looking for a practical Azure Data Engineering book with a strong DevOps perspective, this is the one and only book you need!

Peter Kreyenhop

If you are looking to make the most out of your data this book is a great comprehensive read on how to set up your infrastructure the correct way so that it is reliable and scalable.

Miranda Whurr

This is an excellent case study book companion on how to build a modern data architecture on MS Azure.

Matthias Busch

This book is a light in the tunnel for Azure practitioners.

Krzysztof Kamyczek
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