Data Engineering on Azure

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

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