Triggers in ADF

Triggers in ADF

Azure Data Factory (ADF) is a powerful cloud-based data integration service that allows you to create, schedule, and orchestrate your data workflows. One of the key features that makes ADF so versatile is its ability to automate pipeline executions using triggers. In this article, we'll explore the different types of triggers available in ADF and how to use them to streamline your data operations.

Types of Triggers in ADF

1. Schedule Triggers

Schedule triggers are designed for scenarios where you need to run a pipeline at specific times or on a regular interval. They are ideal for routine data processing tasks, such as daily or hourly data refreshes.

Configuration Options:

  • Start time: When the trigger should start.
  • Recurrence interval: The frequency of the trigger (e.g., every hour, day, week).
  • Time zone: The time zone for the trigger.
  • End time: Optional end time for the trigger.

Example: To create a schedule trigger that runs every day at 6 AM:

{
  "name": "DailyTrigger",
  "properties": {
    "type": "ScheduleTrigger",
    "typeProperties": {
      "recurrence": {
        "frequency": "Day",
        "interval": 1,
        "startTime": "2024-07-15T06:00:00Z",
        "timeZone": "UTC"
      }
    }
  }
}        

2. Tumbling Window Triggers

Tumbling window triggers are similar to schedule triggers but are designed for processing data in contiguous, non-overlapping time intervals. They are perfect for batch processing scenarios where you need to process data in fixed time chunks, such as hourly or daily windows.

Configuration Options:

  • Start time: When the trigger should start.
  • Window size: The size of each window (e.g., hourly, daily).
  • Offset: Optional offset for the window.
  • Retry policy: Optional retry policy.
  • Delay: Optional delay for processing.
  • Maximum concurrency: Optional maximum number of concurrent windows.

Example: To create a tumbling window trigger that runs every hour:

{
  "name": "HourlyTrigger",
  "properties": {
    "type": "TumblingWindowTrigger",
    "typeProperties": {
      "frequency": "Hour",
      "interval": 1,
      "startTime": "2024-07-15T00:00:00Z",
      "timeZone": "UTC"
    }
  }
}        

3. Event Triggers

Event triggers run pipelines in response to specific events, such as when a blob is created or deleted in Azure Blob Storage. This type of trigger is useful for real-time or near-real-time data processing scenarios.

Supported Events:

  • Blob created
  • Blob deleted

Configuration Options:

  • Storage account: The storage account to monitor.
  • Container name: The container within the storage account.
  • Blob path: The path to the blob.
  • Event type: The type of event to monitor (e.g., BlobCreated).

Example: To create an event trigger that runs when a new blob is created:

{
  "name": "BlobCreatedTrigger",
  "properties": {
    "type": "BlobEventTrigger",
    "typeProperties": {
      "scope": "/subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/Microsoft.Storage/storageAccounts/<storage-account>",
      "events": ["Microsoft.Storage.BlobCreated"],
      "blobPathBeginsWith": "/<container>/<path-prefix>/",
      "blobPathEndsWith": ".csv"
    }
  }
}        

4. Manual Triggers

Manual triggers allow you to run a pipeline on-demand. This is useful for ad-hoc scenarios where you need to manually start a pipeline execution.

Usage:

  • You can manually trigger a pipeline execution from the Azure Data Factory UI, PowerShell, CLI, or REST API.

Example: To manually trigger a pipeline execution from the ADF UI:

  1. Navigate to the "Author" tab in ADF.
  2. Select the pipeline you want to run.
  3. Click the "Trigger Now" button.

Conclusion

Triggers in Azure Data Factory provide a flexible and powerful way to automate your data workflows. Whether you need to run pipelines on a schedule, process data in fixed windows, respond to real-time events, or manually kick off executions, ADF has a trigger type that fits your needs. By leveraging these triggers, you can streamline your data operations and ensure your data processes run smoothly and efficiently.

要查看或添加评论,请登录

Kumar Preeti Lata的更多文章

社区洞察

其他会员也浏览了