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New Azure Data Factory self-paced hands-on lab for UI

Опубликовано 31 января, 2018

Senior Program Manager, Information Management

A few weeks back, we announced the public preview release of the new browser-based V2 UI experience for Azure Data Factory. We’ve since partnered with Pragmatic Works, who have been long-time experts in the Microsoft data integration and ETL space, to create a new set of hands on labs that you can now use to learn how to build those DI patterns using ADF V2.

In that repo, you will find data files and scripts in the Deployment folder. There are also lab manual folders for each lab module as well an overview presentation to walk you through the labs. Below you will find more details on each module.

The repo also includes a series of PowerShell and database scripts as well as Azure ARM templates that will generate resource groups that the labs need in order for you to successfully build out an end-to-end scenario, including some sample data that you can use for Power BI reports in the final Lab Module 9.

Here is how the individual labs are divided:

  • Lab 1 - Setting up ADF and Resources, Start here to get all of the ARM resource groups and database backup files loaded properly.
  • Lab 2 - Lift and Shift of SSIS to Azure, Go to this lab if you have existing SSIS packages on-prem that you’d like to migrate directly to the cloud using the ADF SSIS-IR capability.
  • Lab 3 - Rebuilding an existing SSIS job as an ADF pipeline.
  • Lab 4 - Take the new ADF pipeline and enhance it with data from Cloud Sources.
  • Lab 5 - Modernize the DW pipeline by transforming Big Data with HDInsight.
  • Lab 6 - Go to this lab to learn how to create copy workflows in ADF into Azure SQL Data Warehouse.
  • Lab 7 - Build a trigger-based schedule for your new ADF pipeline.
  • Lab 8 - You’ve operationalized your pipeline based on a schedule. Now learn how to monitor and manage that DI process.
  • Lab 9 - Bringing it all Together

Thank you and we hope that you enjoy using the lab to learn how to build scale-out data integration project using Azure Data Factory!