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docs/cicd/deployment-pipelines/intro-to-deployment-pipelines.md

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title: Overview of Fabric deployment pipelines
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description: An introduction to deployment pipelines in the Fabric Application lifecycle management (ALM) tool. Learn which items can be deployed, the structure of a pipeline, and how to pair items.
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ms.topic: overview
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ms.date: 12/15/2025
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ms.date: 07/17/2026
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ms.search.form: Create deployment pipeline, View deployment pipeline, Introduction to Deployment pipelines
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#customer intent: As a developer, I want to learn about deployment pipelines in the Fabric service so that I can manage my development process efficiently.
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* Data Science items:
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* [Machine learning experiments](../../data-science/machine-learning-artifacts-git-deployment-pipelines.md#machine-learning-experiments-and-models-in-deployment-pipelines) *(preview)*
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* [Machine learning models](../../data-science/machine-learning-artifacts-git-deployment-pipelines.md#machine-learning-experiments-and-models-in-deployment-pipelines) *(preview)*
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* [Data Agents](../../data-science/how-to-create-data-agent.md) *(preview)*
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* [Data Agents](../../data-science/how-to-create-data-agent.md)
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* Data Factory items:
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* [Mirrored database](../../mirroring/mirrored-database-cicd.md#mirrored-database-in-deployment-pipelines)
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* [Mount ADF](../../data-factory/tutorial-bring-azure-data-factory-to-fabric.md)
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* [Mirrored snowflake](../../mirroring/snowflake.md) *(preview)*
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* [Airflow](../../data-factory/apache-airflow-jobs-concepts.md) *(preview)*
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* [dbt Job](../../data-factory/dbt-job-overview.md) *(preview)*
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* [Operations Agent](../../data-factory/operations-agent-for-pipelines.md) *(preview)*
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* Real-time Intelligence items:
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* [Activator](../../real-time-intelligence/git-deployment-pipelines.md) *(preview)*
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* [Activator](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Digital twin builder](../../real-time-intelligence/digital-twin-builder/overview.md) *(preview)*
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* [Eventhouse](../../real-time-intelligence/git-deployment-pipelines.md)
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* [EventStream](../../real-time-intelligence/event-streams/eventstream-cicd.md#deploy-eventstream-items-from-one-stage-to-another)
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* [KQL database](../../real-time-intelligence/git-deployment-pipelines.md)
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* [KQL Queryset](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Real-time Dashboard](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Maps](../../real-time-intelligence/map/about-fabric-maps.md)
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* [Event Schema Set](../../real-time-intelligence/schema-sets/create-manage-event-schemas.md) *(preview)*
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* [Maps](/azure/azure-maps/) *(preview)*
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* [Anomaly detection](../../real-time-intelligence/multivariate-anomaly-overview.md) *(preview)*
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* Data Warehouse items:
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* [Warehouse](../../data-warehouse/source-control.md#deployment-pipelines) *(preview)
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* [Warehouse](../../data-warehouse/source-control.md#deployment-pipelines) *(preview)*
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* Mirrored Azure Databricks Catalog
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* Database items:
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* [SQL database](../../database/sql/deployment-pipelines.md) *(preview)*
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* [SQL database](../../database/sql/deployment-pipelines.md)
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* [Cosmos database](../../database/cosmos-db/overview.md) *(preview)*
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* Graph:
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* [Graph Model](../../graph/overview.md)
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* [Graph QuerySet](../../graph/overview.md)
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* CI/CD items:
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* [Variable Library](../variable-library/variable-library-overview.md)
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* Industry solutions:
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* [Healthcare](/industry/healthcare/healthcare-data-solutions/application-lifecycle-management) *(preview)*
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* IQ (preview) items:
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* [Ontology](../../iq/ontology/overview.md) *(preview)*
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* [Plan](../../iq/plan/overview.md) *(preview)*
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## Pipeline structure
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docs/cicd/git-integration/intro-to-git-integration.md

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description: An introduction to integrating Git version control with the Fabric Application lifecycle management (ALM) tool
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ms.reviewer: NimrodShalit
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ms.topic: overview
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ms.date: 06/15/2026
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ms.date: 07/17/2026
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ms.search.form: Git integration supported items, Introduction to Git integration
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#customer intent: As a developer I want to learn about the Git integration feature in Fabric so that my team can collaborate more effectively.
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* Data Science items:
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* [Machine learning experiments](../../data-science/machine-learning-artifacts-git-deployment-pipelines.md#machine-learning-experiments-and-models-git-integration) *(preview)*
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* [Machine learning models](../../data-science/machine-learning-artifacts-git-deployment-pipelines.md#machine-learning-experiments-and-models-git-integration) *(preview)*
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* [Data Agents](../../data-science/how-to-create-data-agent.md) *(preview)*
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* [Data Agents](../../data-science/how-to-create-data-agent.md)
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* Data Factory items:
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* [Mirrored database](../../mirroring/mirrored-database-cicd.md#mirrored-database-git-integration)
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* [Mount ADF](../../data-factory/tutorial-bring-azure-data-factory-to-fabric.md)
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* [Mirrored snowflake](../../mirroring/snowflake.md) *(preview)*
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* [Airflow](../../data-factory/apache-airflow-jobs-concepts.md) *(preview)*
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* [dbt Job](../../data-factory/dbt-job-overview.md) *(preview)*
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* [Operations Agent](../../data-factory/operations-agent-for-pipelines.md) *(preview)*
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* Real-time Intelligence items:
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* [Activator](../../real-time-intelligence/git-deployment-pipelines.md) *(preview)*
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* [Activator](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Eventhouse](../../real-time-intelligence/git-deployment-pipelines.md)
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* [EventStream](../../real-time-intelligence/git-deployment-pipelines.md)
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* [KQL database](../../real-time-intelligence/git-deployment-pipelines.md)
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* [KQL Queryset](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Real-time Dashboard](../../real-time-intelligence/git-deployment-pipelines.md)
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* [Event Schema Set](../../real-time-intelligence/schema-sets/create-manage-event-schemas.md) *(preview)*
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* [Maps](../../real-time-intelligence/map/about-fabric-maps.md)
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* [Event Schema Set](../../real-time-intelligence/schema-sets/create-manage-event-schemas.md) *(preview)*
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* [Digital twin builder](../../real-time-intelligence/digital-twin-builder/overview.md) *(preview)*
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* [Anomaly detection](../../real-time-intelligence/multivariate-anomaly-overview.md) *(preview)*
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* Data Warehouse items:
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* [Cosmos database](../../database/cosmos-db/overview.md) *(preview)*
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* Graph:
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* [Graph in Microsoft Fabric (preview)](../../graph/overview.md)
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* [Graph Model](../../graph/overview.md)
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* [Graph QuerySet](../../graph/overview.md)
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* CI/CD items:
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* [Variable Library](../variable-library/variable-library-overview.md)
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* IQ (preview) items:
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* [Ontology](../../iq/ontology/overview.md) *(preview)*
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* [Plan](../../iq/plan/overview.md) *(preview)*
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* [Healthcare](/industry/healthcare/healthcare-data-solutions/application-lifecycle-management) *(preview)*

docs/cicd/troubleshoot-cicd.md

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**Solution**: To resolve the issue, [commit](./git-integration/git-get-started.md#commit-changes-to-git) changes to Git. If you can't make changes directly to the connected branch, we recommend using the [checkout branch](./git-integration/git-integration-process.md#handling-folder-changes-safely) option. For more information, see [Handling folder changes safely](./git-integration/git-integration-process.md#handling-folder-changes-safely).
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#### My workspace shows uncommitted changes on a report I didn't modify
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**Description of problem**: I notice uncommitted changes for a report in my workspace, even though I didn't manually modify any artifacts.
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**Cause**: This behavior can happen when a report name includes special characters such as `:`, `#`, `[`, or `]`, and the report has a dependency-by-path on a semantic model. Special characters in artifact names cause the system to autocorrect the dependency paths. These automatic adjustments are expected behavior and ensure the report stays correctly linked to its semantic model in the Git branch.
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**Solution**: Commit the changes so the Git branch stays aligned with the artifact's true state. If you want more certainty before committing, use the **Commit to new branch** option in the source control pane, and then compare the differences between:
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* the artifact currently stored in your Git branch
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* the newly generated artifact committed to the new branch
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Review the diff to confirm that only dependency paths were adjusted and that no unintended logic or structural changes were introduced. This workflow gives you a safe validation step before you merge the changes into your working branch.
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### Commit issues
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#### The Commit button is disabled

docs/data-engineering/how-to-use-notebook.md

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- You can also operate on the files/folders same with the Built-in resources folder.
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- The Environment resource path is automatically mounted to the notebook cluster. You can use the relative path **/env** to access the environment resources.
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Fabric Environments support two library publishing modes that affect how libraries are delivered to your notebook sessions:
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- **Quick mode** publishes in about 5 seconds and installs libraries when your notebook session starts. Quick mode can override library versions published through Full mode, but only for the current session.
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- **Full mode** creates a stable, reproducible library snapshot. Publishing typically takes 3 to 6 minutes, and session startup adds 1 to 3 minutes for dependency deployment. Using Full mode with a [custom live pool](custom-live-pools-overview.md) can bring session start times back to approximately 5 seconds while maintaining the stable snapshot.
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Fabric environments support two library publishing modes, Quick and Full, that affect how libraries are delivered to your notebook sessions. Quick mode installs libraries at session start for fast iteration, while Full mode creates a stable, reproducible snapshot. For details on each mode, including publish times, supported library sources, and how the modes interact, see [Manage libraries in Fabric environments](environment-manage-library.md#select-publish-mode-for-libraries).
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### Use environment libraries in notebooks
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- **Quick mode for iterative development**: Use Quick mode when you're actively experimenting in notebooks and need fast library iteration. Libraries install at session start with minimal publish time.
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- **Full mode for reproducibility**: Use Full mode when you need consistent library versions across collaborators, scheduled runs, or pipeline jobs. The snapshot ensures every session starts with the same dependencies.
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- **Full mode with a custom live pool for fast and stable sessions**: When both fast session startup and reproducibility matter, configure Full mode with a [custom live pool](custom-live-pools-overview.md). This combination achieves approximately 5-second session starts while preserving the stable library snapshot.
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- **Full mode with a custom live pool for fast and stable sessions**: When both fast session startup and reproducibility matter, configure Full mode with a [custom live pool](custom-live-pools-overview.md). This combination gives you fast session starts while preserving the stable library snapshot.
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> [!NOTE]
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> Reading/writing with a relative path is not functioning in a [High concurrency session](../data-engineering/configure-high-concurrency-session-notebooks.md).

docs/data-engineering/toc.yml

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- name: Service details and limitations
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- name: Schedule functions
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- name: Materialized lake views
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- name: Overview
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---
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title: Schedule a User Data Function
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description: Learn how to configure a scheduled trigger for a User Data Function in Microsoft Fabric using the built-in Job Scheduler.
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ms.reviewer: mksuni
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ms.topic: how-to
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---
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# Schedule a User Data Function
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Microsoft Fabric Job Scheduler enables you to run User Data Functions (UDFs) on a recurring schedule without requiring a pipeline, notebook, or external orchestration service. You can configure schedules directly on a User Data Function, specify recurrence patterns, pass function parameters, and monitor executions from the Fabric monitoring experience.
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## Prerequisites
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- You have Contributor or higher permissions on the workspace.
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- The function is tested successfully by using manual invocation.
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Schedule User Data Functions to automate recurring business and operational tasks. Common scenarios include:
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- **Customer feedback processing** – Analyze new feedback, generate sentiment scores, and store results.
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- **Business event generation** – Detect changes in operational data and publish business events for downstream applications.
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- **Scheduled cleanup** – Archive old records, remove temporary files, or perform routine maintenance tasks.
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1. Open the User Data Function item.
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1. Select **Settings**.
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1. Select **Schedule**.
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- Edit or disable schedules
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| Setting | Description |
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|----------|-------------|
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| Controller | Variable used to control execution from Variable library |
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| Repeat | Defines the recurrence pattern |
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| Interval | Frequency interval |
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| Start date and time | When the schedule begins |
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| End date and time | When the schedule expires |
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| Time zone | Time zone for schedule execution |
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| Parameters | Input parameters passed to the function |
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> [!IMPORTANT]
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You can configure email notifications for failed scheduled runs.
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1. Save the configuration.
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Fabric sends email notifications whenever a scheduled execution fails.
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## Next steps
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- Learn about User Data Functions.
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- Learn about Job Scheduler in Microsoft Fabric.
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- Monitor and troubleshoot Fabric workloads using the Monitor hub.

docs/fundamentals/job-scheduler.md

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### Important considerations
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- **Available for Fabric items that support scheduling**: Notifications are available across all Fabric items that support scheduling (for example, Pipelines, Notebooks, and Dataflows Gen2).
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- **Available for Fabric items that support scheduling**: Notifications are available across all Fabric items that support scheduling, such as Pipelines, Notebooks, User data functions, and Dataflows Gen2.
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- **Applies to all schedules**: Notifications are configured at the item level and the settings apply across all schedules for the item.
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- **Scheduled runs only**: Notifications are sent only for failures from scheduled runs. Notifications aren't sent for manually triggered runs.
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- **Recipients**: Notifications can be sent to users or groups in your Microsoft Entra tenant, including internal users and B2B guest users. Direct external email addresses aren’t supported.

docs/iq/ontology/how-to-create-operations-agent.md

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* **Agent instructions**: Add guidance for the agent's behavior. For example, *Monitor the freezer temperature and keep the temperature below 20.*
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* **Knowledge**: Select **Add data** and choose your ontology item.
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* **Actions** (optional): Define one or more actions the agent can recommend, such as *NotifyStoreOperations* with parameters like *StoreId* and *FreezerId*. If you add an action, configure it as described in [Configure an operations agent](../../real-time-intelligence/operations-agent-actions.md).
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1. Select **Generate playbook**. Review the concepts and rules in the playbook and confirm they reference the expected ontology entity types (such as *Store*, *Product*, and *Freezer*) and properties.
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1. Select **Start** in the toolbar to start the agent.
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