Export query data to Microsoft Fabric

You can use the Viva Insights connector to transform and export your Viva Insights query data to Microsoft Fabric using Dataflow Gen2.

Once your data's in Microsoft Fabric, you can automate the export of it to other cloud storage locations using Copy job. Learn more.

Dataflow Gen2 data destinations

With Dataflow Gen2, you can load your data into destinations such as Azure SQL and Fabric Lakehouse. Dataflows automatically write the data to that location, making it ready for use in analytics and reporting. Learn more.

Scheduled refresh

You can automate the export of new data by scheduling your Viva Insights query and the Dataflow to refresh.

In Viva Insights, after you run a query, set it to auto-refresh. You can do that during query setup, or in Analysis results by turning on Auto-refresh.

Then within your Dataflow, set up a scheduled refresh to run on specific days and times. Learn how.

Dataflows also support Incremental refresh, which lets you only refresh new or updated data to help reduce refresh time, reduce resource usage, and improve reliability. Learn more.

You should schedule the Dataflows refresh around Tuesday 8 a.m. PST, to occur after the Viva Insights data weekly refresh which typically happens over the weekend.

Dataflow Gen2 pricing

When you refresh or publish a Dataflow, Fabric Capacity Units are consumed. To see the detailed rate structure and usage examples, refer to the official pricing information.

Note

If your organization uses Privileged Identity Management (PIM), you might need to regularly elevate and refresh credentials in the dataflows settings to refresh the data.

Steps to connect

  1. Open Microsoft Fabric. Go to https://app.fabric.microsoft.com and navigate to the workspace where you want to create the Dataflow.

  2. Select Create a new Dataflow, then select New > Dataflow Gen2.

  3. Select Get data.

    Screenshot that shows how to set up a new item in the connector.

    On the Get data page, search for "Viva Insights" under Online Services. Select the Viva Insights connector to open the connection settings.

    Screenshot that shows connector settings.

  4. Enter connection settings.

  • Partition Identifier: Required — enter your partition ID.

    • To find the partition ID, go to Analysis results in Viva Insights, find the query you want to export, and select the Link icon.

  • Query Name: Leave blank.

  • Query Identifiers: Required — enter your query ID.

    • To find the query ID, go to Analysis results in Viva Insights, find the query you want to export, and select the Link icon.

  • Under Advanced options:

    • Schema Type: Select Pivoted.
    • Data Granularity: Select Row-level data.
    • Table name: Leave blank, unless you're trying to connect to a report that produces multi-table outputs like Glint and organizational insights, Copilot business impact, or Skills landscape. If so, enter the table name you want to connect to. You can find the available table names in the table below.

  • Under connection credentials:

    • For Authentication kind, select Organizational account. Sign in with your account when prompted.

  • Select Next.

    Screenshot that shows how to enter the settings.

  1. Validate and transform your data. Use the Power Query editor to preview, filter, and transform the data as needed.

  2. Save and refresh. Save the Dataflow to your workspace and set up a refresh schedule to keep your data up to date.

Queries with multi-table outputs

Most queries only generate a single table output, but the queries below create multi-table outputs. If you're trying to connect to these, you'll need to identify which table you want to connect to. Enter this information in the Table Name field when you connect. You'll need to create a connection for each table you want to connect to.

Query Available table names
Glint and organizational insights MetricOutput
PeopleHistorical
Survey
SurveyMetadata
CategoryToMetricMapping
Copilot business impact MetricOutput
PeopleHistorical
GenericDescriptiveData
Skills landscape MetricOutput
PeopleHistorical
GenericDescriptiveData
SkillsLibrary
HierarchicalSkills
RelatedSkills
Consumption Dashboard - Weekly, De-Identified AiConsumptionWeeklyExportData_HR
AiConsumptionWeeklyExportData_UserAIConsumptionActivity
AiConsumptionWeeklyExportData_AIConsumptionPlans
Consumption Dashboard - Daily, De-Identified AiConsumptionDailyExportData_HR
AiConsumptionDailyExportData_UserAIConsumptionActivity
AiConsumptionDailyExportData_AIConsumptionPlans
Consumption Dashboard - Weekly, Identified IdentifiableAiConsumptionWeeklyExportData_UserAIConsumptionActivity
IdentifiableAiConsumptionWeeklyExportData_AIConsumptionPlans
Consumption Dashboard - Daily, Identified IdentifiableAiConsumptionDailyExportData_UserAIConsumptionActivity
IdentifiableAiConsumptionDailyExportData_AIConsumptionPlans
GitHub Copilot Export - Weekly, De-Identified GitHubCopilotWeeklyExportData_HR
GitHubCopilotWeeklyExportData_GitHubCopilotActivity
GitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Model
GitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Model_Feature
GitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Feature
GitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Feature
GitHub Copilot Export - Daily, De-Identified GitHubCopilotDailyExportData_HR
GitHubCopilotDailyExportData_GitHubCopilotActivity
GitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Model
GitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Model_Feature
GitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Feature
GitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Feature
GitHub Copilot Export - Weekly, Identified IdentifiableGitHubCopilotWeeklyExportData_GitHubCopilotActivity
IdentifiableGitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Model
IdentifiableGitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Model_Feature
IdentifiableGitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Feature
IdentifiableGitHubCopilotWeeklyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Feature
GitHub Copilot Export - Daily, Identified IdentifiableGitHubCopilotDailyExportData_GitHubCopilotActivity
IdentifiableGitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Model
IdentifiableGitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Model_Feature
IdentifiableGitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Language_Feature
IdentifiableGitHubCopilotDailyExportData_GitHubCopilotBreakdownActivity_by_PersonId_MetricDate_Feature
Agent Dashboard Export - Weekly, De-Identified ADBExportWeeklyData_HR
ADBExportWeeklyData_AgentMetadata
ADBExportWeeklyData_UserAgentActivity
ADBExportWeeklyData_UserAgentHostAppLicenseStateBillingPolicyActivity
Agent Dashboard Export - Weekly, Identified IdentifiableADBExportWeeklyData_AgentMetadata
IdentifiableADBExportWeeklyData_UserAgentActivity
IdentifiableADBExportWeeklyData_UserAgentHostAppLicenseStateBillingPolicyActivity
Agent 365 Dashboard Export - Weekly, De-Identified A365WeeklyExportData_HR
A365WeeklyExportData_A365AgentMetadata
A365WeeklyExportData_A365UserAgentActivity
Agent 365 Dashboard Export - Daily, De-Identified A365DailyExportData_HR
A365DailyExportData_A365AgentMetadata
A365DailyExportData_A365UserAgentActivity
Agent 365 Dashboard Export - Weekly, Identified IdentifiableA365WeeklyExportData_A365AgentMetadata
IdentifiableA365WeeklyExportData_A365UserAgentActivity
Agent 365 Dashboard Export - Daily, Identified IdentifiableA365DailyExportData_A365AgentMetadata
IdentifiableA365DailyExportData_A365UserAgentActivity

Move Viva Insights query data to other cloud locations using Copy job

After you load Viva Insights query data into Microsoft Fabric using Dataflow Gen2, use Copy job to move that data to other cloud storage locations. Copy job is a Microsoft Fabric feature that enables low-code, scalable data movement between supported data stores. This is useful when you require data in a location outside of Fabric.

Prerequisites

Before you set up a Copy job, make sure you have:

  1. Viva Insights query data already loaded into a Fabric destination such as a Lakehouse table using Dataflow Gen2

  2. A Microsoft Fabric workspace with sufficient capacity

  3. A connection to the target cloud storage location already configured in Fabric, including any required credentials

How to move data with Copy job

  1. Open Microsoft Fabric. Go to https://app.fabric.microsoft.com and navigate to the workspace where your Dataflow Gen2 output is stored.

  2. Create a new Copy job. Select New > Copy Job from the workspace.

  3. Configure the source. Select the data source where your Viva Insights data was written by the Dataflow.

  4. Configure the destination. Choose your target cloud location and select the connection that has the appropriate credentials.

  5. Save and run. Save the Copy job, then select Run to start the transfer. You can also schedule it to run automatically on a recurring basis.

Coordinate scheduling with your Dataflow

For a fully automated pipeline, schedule the Copy job to run after the Dataflow Gen2 refresh completes. A recommended end-to-end sequence is:

  1. Viva Insights query auto-refresh runs over the weekend.

  2. Dataflow Gen2 scheduled refresh runs on Tuesday at around 8:00 AM PST, once the updated Viva Insights data is available.

  3. Copy job runs after the Dataflow refresh completes, pushing the updated data to the target cloud location.

For additional guidance on using Dataflow Gen2 in Microsoft Fabric, learn how to create your first Dataflow Gen2.

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