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)

Extracting data from a Big Data Cluster

In this recipe, you are going to complete the development of an SSIS solution used to extract data from a database hosted on a Big Data Cluster instance in Azure and load it into a database hosted on the local SQL Server instance.

The purpose of this package is to reduce the size of the data that actually needs to be transferred from the external source into the line-of-business database, by restricting the remote source set to only contain data that does not yet exist in the local database.

In general, determining the delta hardly represents a task worthy of the capabilities available in Big Data Clusters or a typical case for using Spark; however, it does represent a very significant element in efficient data warehousing.

Getting ready

This recipe assumes that you have completed the preceding recipe in this chapter, entitled Loading data into a Big Data Cluster, and that the expected data is available in the dbo.NewPeople external...