Book Image

Azure Data Engineering Cookbook

By : Ahmad Osama
Book Image

Azure Data Engineering Cookbook

By: Ahmad Osama

Overview of this book

Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You’ll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure.
Table of Contents (11 chapters)

Chapter 7: Azure Data Factory Integration Runtime

The Azure Data Factory Integration Runtime (IR) is the compute infrastructure that is responsible for executing data flows, pipeline activities, data movement, and SQL Server Integration Services (SSIS) packages. There are three types of IR: Azure, self-hosted, and Azure SSIS.

The Azure IR is the default IR that is created whenever a new data factory is created. It can process data flows, data movement, and activities.

A self-hosted IR can be installed on-premises or on a virtual machine running the Windows OS. A self-hosted IR can be used to work with data on-premises or in the cloud. It can be used for data movement and activities.

The Azure SSIS IR is used to lift and shift existing SQL SSIS.

In this chapter, we'll learn how to use a self-hosted IR and Azure SSIS IR through the following recipes:

  • Configuring a self-hosted IR
  • Configuring a shared self-hosted IR
  • Migrating an SSIS package to Azure...