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)

Creating a cluster in our workspace

A cluster is necessary to manipulate and transform data with Databricks. It is composed of a minimum of two machines:

  • A driver node: Receives the commands and dispatches them to a worker.
  • A worker node: Receives and executes the commands. We can use multiple workers that will execute the command in parallel.

There are also two types of clusters:

  • Interactive: A cluster that is started manually. It is used to do interactive queries in a notebook, or another program connected to it, such as Power BI.
  • Automated: A cluster created automatically to run a job and stopped after it. For example, this type of cluster is used when we use a Databricks activity in Azure Data Factory.

Let's create a cluster in our Databricks workspace now.

Getting ready

As with every recipe in this chapter, you will need to upgrade your trial Azure subscription to a Pay-As-You-Go subscription if this is not what you have been using...