Book Image

ETL with Azure Cookbook

By : Christian Cote, Matija Lah, Madina Saitakhmetova
Book Image

ETL with Azure Cookbook

By: Christian Cote, Matija Lah, Madina Saitakhmetova

Overview of this book

ETL is one of the most common and tedious procedures for moving and processing data from one database to another. With the help of this book, you will be able to speed up the process by designing effective ETL solutions using the Azure services available for handling and transforming any data to suit your requirements. With this cookbook, you’ll become well versed in all the features of SQL Server Integration Services (SSIS) to perform data migration and ETL tasks that integrate with Azure. You’ll learn how to transform data in Azure and understand how legacy systems perform ETL on-premises using SSIS. Later chapters will get you up to speed with connecting and retrieving data from SQL Server 2019 Big Data Clusters, and even show you how to extend and customize the SSIS toolbox using custom-developed tasks and transforms. This ETL book also contains practical recipes for moving and transforming data with Azure services, such as Data Factory and Azure Databricks, and lets you explore various options for migrating SSIS packages to Azure. Toward the end, you’ll find out how to profile data in the cloud and automate service creation with Business Intelligence Markup Language (BIML). By the end of this book, you’ll have developed the skills you need to create and automate ETL solutions on-premises as well as in Azure.
Table of Contents (12 chapters)

Chapter 3: Creating and Using SQL Server 2019 Big Data Clusters

In this chapter, you will learn how to prepare and deploy a SQL Server 2019 Big Data Cluster. You will learn how Big Data Clusters can be used in data management operations, implemented as SQL Server Integration Services (SSIS) solutions. We will show you how to use SSIS to load data into a Big Data Cluster Hadoop Distributed File System (HDFS) file store and how to perform basic data processing operations on the Spark platform, and we will finish the chapter by demonstrating how to retrieve data from a Big Data Cluster, to be used in a database hosted locally.

After you have created and deployed your new Big Data Cluster instance in Azure, we are also going to show you how to efficiently maintain your development environment by suspending specific Azure resources when they are not used, which will allow you to reduce the cost of your Azure subscription.

This chapter covers the following recipes that will get you...