You're reading from Azure Data Engineering Cookbook Get well versed in various data engineering techniques in Azure using this recipe-based guide

Product type Paperback

Published in Sep 2022

Publisher Packt

ISBN-13 9781803246789

Length 608 pages

Edition 2nd Edition

Languages

SQL

Tools

Azure

Concepts

Data Engineering

Authors (3):

Ahmad Osama

Nagaraj Venkatesan

Luca Zanna

View More author details

Table of Contents (16) Chapters

Preface

1. Chapter 1: Creating and Managing Data in Azure Data Lake

2. Chapter 2: Securing and Monitoring Data in Azure Data Lake FREE CHAPTER

3. Chapter 3: Building Data Ingestion Pipelines Using Azure Data Factory

4. Chapter 4: Azure Data Factory Integration Runtime

5. Chapter 5: Configuring and Securing Azure SQL Database

6. Chapter 6: Implementing High Availability and Monitoring in Azure SQL Database

7. Chapter 7: Processing Data Using Azure Databricks

8. Chapter 8: Processing Data Using Azure Synapse Analytics

9. Chapter 9: Transforming Data Using Azure Synapse Dataflows

10. Chapter 10: Building the Serving Layer in Azure Synapse SQL Pool

11. Chapter 11: Monitoring Synapse SQL and Spark Pools

12. Chapter 12: Optimizing and Maintaining Synapse SQL and Spark Pools

13. Chapter 13: Monitoring and Maintaining Azure Data Engineering Pipelines

14. Index

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Patching a self-hosted IR

A self-hosted IR is a key component in Azure Data Factory, facilitating data movement between on-premises data sources and Azure. The self-hosted IR is configured by installing the Microsoft Integration Runtime installer on a machine that can connect to an on-premises data source and Azure. Keeping the self-hosted IR updated with the latest version is of the utmost importance to keep it bug-free and secure.

By default, a self-hosted IR has automatic updates enabled, which will apply the patches and keep the self-hosted IR updated. However, many users of Azure Data Factory would prefer to apply the patches themselves, as they would like to test their data transfer tasks and pipelines in a User Acceptance Test (UAT) environment with the latest patches, before running them in a production environment. So, in this recipe we will perform the following: