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)

Writing in Azure SQL Server

So far, we have read data from the internet and stored it in a Delta Lake table. Delta tables are fine as a consumption layer, but they have some caveats:

  • We need to have a cluster running to query the data. This can be costly at times.
  • Queries take longer than a regular database to get back because the cluster uses a distributed process: driver to workers – especially for small volumes.
  • There's no native row-level security or dynamic data masking as we have in SQL Server.
  • There are no schemas, only databases and tables. This might be an issue for certain applications.

Our ETLInAzure.StateIncome table holds only 52 rows. We have used Databricks to import the data from the internet and stored it in Delta Lake. To use it in our applicative database – AdventureWorksLT – we will copy the transformed data back to SQL Server.

In this recipe, we will save our ETLInAzure.StateIncome table created in the previous...