Manage Azure Data Factory studio preview experience

APPLIES TO: Azure Data Factory Azure Synapse Analytics

You can choose whether you would like to enable preview experiences in your Azure Data Factory.

How to enable/disable preview experience

There are two ways to enable preview experiences.

  1. In the banner seen at the top of the screen, you can click Open settings to learn more and opt in.

    Screenshot of Azure Data Factory home page with an Opt-in option in a banner at the top of the screen.

  2. Alternatively, you can click the Settings button.

    Screenshot of Azure Data Factory home page highlighting Settings gear in top right corner.

    After opening Settings, you will see an option to turn on Azure Data Factory Studio preview update.

    Screenshot of Settings panel highlighting button to turn on Azure Data Factory Studio preview update.

    Toggle the button so that it shows On and click Apply.

    Screenshot of Settings panel showing Azure Data Factory Studio preview update turned on and the Apply button in the bottom left corner.

    Your data factory will refresh to show the preview features.

    Similarly, you can disable preview features with the same steps. Click Open settings to opt out or click the Settings button and unselect Azure Data Factory Studio preview update.

    Screenshot of Azure Data Factory home page with an Opt-out option in a banner at the top of the screen and Settings gear in the top right corner of the screen.

Note

Enabling/disabling preview updates will discard any unsaved changes.

Current Preview Updates

Dataflow data first experimental view

Pipeline experimental view

Monitoring experimental view

Dataflow data-first experimental view

UI (user interfaces) changes have been made to mapping data flows. These changes were made to simplify and streamline the dataflow creation process so that you can focus on what your data looks like.

The dataflow authoring experience remains the same as detailed here, except for certain areas detailed below.

To see the data-first experimental view, you will need to follow these steps to enable it. By default, users will see the Classic style.

Note

To enable the data-first view, you will need to enable the preview experience in your settings and you will need an active Data flow debug session.

In your data flow editor, you can find several canvas tools on the right side like the Search tool, Zoom tool, and Multi-select tool.

Screenshot of the data flow editing canvas.

You will see a new icon under the Multi-select tool. This is how you can toggle between the Classic and the Data-first views.

Screenshot of data flow canvas options with button to switch views highlighted.

Configuration panel

The configuration panel for transformations has now been simplified. Previously, the configuration panel showed settings specific to the selected transformation. Now, for each transformation, the configuration panel will only have Data Preview that will automatically refresh when changes are made to transformations.

Screenshot of the configuration panel with only a Data preview tab.

If no transformation is selected, the panel will show the pre-existing data flow configurations: Parameters and Settings.

Transformation settings

Settings specific to a transformation will now show in a pop-up instead of the configuration panel. With each new transformation, a corresponding pop-up will automatically appear.

Screenshot of a pop-up with settings specific to the data flow transformation.

You can also find the settings by clicking the gear button in the top right corner of the transformation activity.

Screenshot of a data flow source transformation with the settings gear in the top right corner highlighted.

Data preview

If debug mode is on, Data Preview in the configuration panel will give you an interactive snapshot of the data at each transform. Data preview now includes Elapsed time (seconds) to show how long your data preview took to load. Columns can be rearranged by dragging a column by its header. You can also sort columns using the arrows next to the column titles and you can export data preview data using Export to CSV on the banner above column headers.

Screenshot of Data preview with Export button in the top right corner of the banner and Elapsed Time highlighted in the bottom left corner of the screen.

Pipeline experimental view

UI (user interface) changes have been made to activities in the pipeline editor canvas. These changes were made to simplify and streamline the pipeline creation process.

Dynamic content flyout

A new flyout has been added to make it easier to set dynamic content in your pipeline activities without having to use the expression builder. The dynamic content flyout is currently supported in these activities and settings:

Activity Setting name
Azure Function Function Name
Databricks-Notebook Notebook path
Databricks-Jar Main class name
Databricks-Python Python file
Fail Fail message
Fail Error code
Web Url
Webhook Url
Wait Wait time in seconds
Filter Items
Filter Conditions
ForEach Items
If/Switch/Until Expression

In supported activities, you will see an icon next to the setting. Clicking this will open up the flyout where you can choose your dynamic content.

Screenshot of the new dynamic content flyout icon.

Screenshot of the new dynamic content flyout with dynamic content options to choose.

Monitoring experimental view

UI (user interfaces) changes have been made to the monitoring page. These changes were made to simplify and streamline your monitoring experience. The monitoring experience remains the same as detailed here, except for items detailed below.

Error message relocation to Status column

To make it easier for you to view errors when you see a Failed pipeline run, error messages have been relocated to the Status column.

Find the error icon in the pipeline monitoring page and in the pipeline Output tab after debugging your pipeline.

Screenshot of the new error message location in pipeline debug output.

Screenshot of the new error message location in pipeline monitoring details.

Container view

When monitoring your pipeline run, you have the option to enable the container view, which will provide a consolidated view of the activities that ran. This view is available in the output of your pipeline debug run and in the detailed monitoring view found in the monitoring tab.

How to enable the container view in pipeline debug output

In the Output tab in your pipeline, there is a new dropdown to select your monitoring view.

Screenshot of the drop-down menu to select the monitoring view.

Select Hierarchy to see the new hierarchy view. If you have iteration or conditional activities, the nested activities will be grouped under parent activity.

Screenshot of the container monitoring view.

Click the button next to the iteration or conditional activity to collapse the nested activities for a more consolidated view.

Screenshot of the container monitoring  collapsed view.

How to enable the container view in pipeline monitoring

In the detailed view of your pipeline run, there is a new dropdown to select your monitoring view next to the Status filter.

Screenshot of the drop-down menu to select the monitoring view in pipeline monitoring view.

Select Container to see the new container view. If you have iteration or conditional activities, the nested activities will be grouped under the parent activity.

Screenshot of the container monitoring view in pipeline monitoring.

Click the button next to the iteration or conditional activity to collapse the nested activities for a more consolidated view.

Screenshot of the collapsed container monitoring view.

Simplified default monitoring view

The default monitoring view has been simplified with fewer default columns. You can add/remove columns if you’d like to personalize your monitoring view. Changes to the default will be cached.

Screenshot of the new default column view on the monitoring page.

Default columns

Column name Description
Pipeline Name Name of the pipeline
Run Start Start date and time for the pipeline run (MM/DD/YYYY, HH:MM:SS AM/PM)
Duration Run duration (HH:MM:SS)
Triggered By The name of the trigger that started the pipeline
Status Failed, Succeeded, In Progress, Cancelled, or Queued
Parameters Parameters for the pipeline run (name/value pairs)
Error If the pipeline failed, the run error
Run ID ID of the pipeline run

You can edit your default view by clicking Edit Columns.

Screenshot of the Edit Columns button in the center of the top row.

Add columns by clicking Add column or remove columns by clicking the trashcan icon.

Screenshot of the Add column button and trashcan icon to edit column view.

Provide feedback

We want to hear from you! If you see this pop-up, please let us know your thoughts by providing feedback on the updates you've tested.

Screenshot of the feedback survey where user can select between one and five stars.

Next steps