Azure Data Lake Analytics
Dynatrace ingests metrics from Azure Metrics API for Azure Data Lake Analytics. You can view metrics for each service instance, split metrics into multiple dimensions, and create custom charts that you can pin to your dashboards.
Prerequisites
- Dynatrace version 1.199+
- Environment ActiveGate version 1.195+
Enable monitoring
To enable monitoring for Azure Data Lake Analytics, you first need to set up integration with Azure Monitor.
Add the service to monitoring
In order to view the service metrics, you must add the service to monitoring in your Dynatrace environment.
Monitor resources based on tags
You can choose to monitor resources based on existing Azure tags, as Dynatrace automatically imports them from service instances.
To monitor resources based on tags
- Go to Settings > Cloud and virtualization > Azure and select the Azure instance.
- For Resource monitoring method, select Monitor resources based on tags.
- Enter the Key and Value.
- Select Save.
Note: To import the Azure tags automatically into Dynatrace, enable Capture Azure tags automatically.
Configure service metrics
Once you add a service, Dynatrace starts automatically collecting a suite of metrics for this particular service. These are recommended metrics.
Recommended metrics:
- Are enabled by default
- Can't be disabled
- Can have recommended dimensions (enabled by default, can't be disabled)
- Can have optional dimensions (disabled by default, can be enabled).
Apart from the recommended metrics, most services have the possibility of enabling optional metrics.
Optional metrics:
- Can be added and configured manually
View service metrics
Once you add the service to monitoring, you can view the service metrics in your Dynatrace environment either on your dashboard page or on the custom device overview page.
Import preset dashboards
Dynatrace provides preset Azure dashboards that you can import from GitHub to your environment's Dashboards page.
Note: To save a preset dashboard locally, create a new JSON file on your local machine and copy-paste the content of the JSON file from GitHub into the new file.
Once you save a preset dashboard locally, there are two ways to import it.
Available metrics
Name | Description | Dimensions | Unit | Recommended |
---|---|---|---|---|
JobEndedSuccess | Count of successful jobs | containerName | Count | ✔️ |
JobEndedFailure | Count of failed jobs | containerName | Count | ✔️ |
JobEndedCancelled | Count of cancelled jobs | Count | ✔️ | |
JobAUEndedSuccess | Total AU time for successful jobs | Second | ✔️ | |
JobAUEndedFailure | Total AU time for failed jobs | Second | ✔️ | |
JobAUEndedCancelled | Total AU time for cancelled jobs | Second | ✔️ | |
JobStage | Progress of jobs | Count |