Azure Data Architecture Guide – Blog #9: Extract, transform, load (ETL)

01/09/2019

This is our ninth and final blog entry exploring the Azure Data Architecture Guide. The previous entries for this blog series are:

Like all the previous posts in this series, we'll work from a technology implementation seen directly in our customer engagements. The example can help lead you to the ADAG content to make the right technology choices for your business.

Extract, transform, load (ETL)

In this example, the web application logs and custom telemetry are captured with Application Insights, sent to Azure Storage blobs, and then the ETL pipeline is created, scheduled, and managed using Azure Data Factory. The SSIS packages are deployed to Azure--with the Azure-SSIS integration runtime (IR) in Azure Data Factory--to apply data transformation as a step in the ETL pipeline, before loading the transformed data into Azure SQL Database.

ETL