Data Engineeringon Azure

Vlad Riscutia

ISBN 10: 1617298921 / ISBN 13: 9781617298929

Published by Manning Publications, US, 2022

About this Item

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 TechnologyBuild 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.

Seller Inventory # LU-9781617298929

Bibliographic Details

• Title: Data Engineeringon Azure
• Publisher: Manning Publications, US
• Publication Date: 2022
• Language: English
• ISBN 10: 1617298921
• ISBN 13: 9781617298929
• Binding: Paperback
• Condition: New

About this title

Synopsis

Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure.

Summary
In Data Engineering on Azure you will learn how to:

    Pick the right Azure services for different data scenarios
    Manage data inventory
    Implement production quality data modeling, analytics, and machine learning workloads
    Handle data governance
    Using DevOps to increase reliability
    Ingesting, storing, and distributing data
    Apply best practices for compliance and access control

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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 book
In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms.

What's inside

    Data inventory and data governance
    Assure data quality, compliance, and distribution
    Build automated pipelines to increase reliability
    Ingest, store, and distribute data
    Production-quality data modeling, analytics, and machine learning

About the reader
For data engineers familiar with cloud computing and DevOps.

About the author
Vlad Riscutia is a software architect at Microsoft.

Table of Contents

1 Introduction
PART 1 INFRASTRUCTURE
2 Storage
3 DevOps
4 Orchestration
PART 2 WORKLOADS
5 Processing
6 Analytics
7 Machine learning
PART 3 GOVERNANCE
8 Metadata
9 Data quality
10 Compliance
11 Distributing data

About the Author

Vlad Riscutia is a software architect at Microsoft.