Data Factory metrics
Azure Data Factory is a cloud-based data integration and orchestration service that allows organizations to create, schedule, and orchestrate workflows (data pipelines) that integrate and process data from various sources to destinations. Use the Azure Resource Manager to gather metrics for this resource, then ensure your cloud platform is configured in SolarWinds Observability SaaS to collect this resource type's data. See Add an Azure cloud account.
Depending on the subscription pricing tier of your Azure account or its resources, additional metrics may be available for this entity. To collect additional Azure metrics, select the premium pricing tier when configuring your Azure namespaces.
Many of the collected metrics from Data Factory entities are displayed as widgets in SolarWinds Observability explorers; additional metrics may be collected and available in the Metrics Explorer. You can also create an alert for when an entity's metric value moves out of a specific range. See Entities in SolarWinds Observability SaaS for information about entity types in SolarWinds Observability SaaS.
The following table lists azure.data.factory in the search box.
| Metric | Units | Description |
|---|---|---|
sw.metrics.healthscore | Percent (%) | Health score. A health score provides real-time insight into the overall health and performance of your monitored entities. The health score is calculated based on anomalies detected for the entity, alerts triggered for the entity's metrics, and the status of the entity. The health score is displayed as a single numerical value that ranges from a Good (70-100) to Moderate (40-69) to Bad (0-39) distinction. To view the health score for Data Factory entities in the Metrics Explorer, filter the |
azure.data.factory.PipelineFailedRuns | Count | The number of pipeline runs that have failed due to errors or unexpected conditions during execution. This helps identify issues in data workflows and troubleshoot failures. |
azure.data.factory.PipelineSucceededRuns | Count | The number of pipeline runs that have successfully completed without errors. This metric helps monitor the reliability and efficiency of data pipelines. |
azure.data.factory.PipelineCancelledRuns | Count | The number of pipeline runs that were manually or automatically canceled before completion. This can be useful for tracking interruptions in data processing. |
azure.data.factory.ActivityCancelledRuns | Count | The number of individual activities within a pipeline that were canceled before execution or completion. This helps monitor workflow interruptions at the activity level. |
azure.data.factory.ActivitySucceededRuns | Count | The number of activities within a pipeline that have successfully completed without errors. This metric helps assess the effectiveness of individual tasks in a data pipeline. |
azure.data.factory.ActivityFailedRuns | Count | The number of activities within a pipeline that have failed due to errors or unexpected conditions. This helps pinpoint specific issues within a pipeline execution. |
azure.data.factory.TriggerFailedRuns | Count | The number of trigger runs that have failed due to errors or unexpected conditions during execution. This helps identify issues in automated data workflows. |
azure.data.factory.TriggerSucceededRuns | Count | The number of trigger runs that have successfully completed without errors. This metric helps monitor the reliability and efficiency of scheduled or event-driven triggers. |
azure.data.factory.TriggerCancelledRuns | Count | The number of trigger runs that were manually or automatically canceled before completion. This can be useful for tracking interruptions in data processing. |
azure.data.factory.MaxAllowedResourceCount | Count | The maximum number of resources allowed within an Azure Data Factory instance. This metric helps monitor resource allocation limits. |
azure.data.factory.ResourceCount | Count | The total number of resources currently in use within an Azure Data Factory instance. This metric helps track resource consumption and availability. |
azure.data.factory.FactorySizeInGbUnits | Count | The total size of the Azure Data Factory instance in gigabyte units. This metric helps monitor storage and processing capacity. |
azure.data.factory.IntegrationRuntimeCpuPercentage | Percent (%) | The percentage of CPU utilization for the integration runtime. Higher values may indicate increased workload or potential performance bottlenecks. |
azure.data.factory.IntegrationRuntimeAvailableMemory | bytes | The amount of available memory for the integration runtime. This metric helps monitor resource usage and ensure optimal performance. |
azure.data.factory.IntegrationRuntimeAvailableNodeNumber | Count | The number of available nodes in the integration runtime. This metric is useful for assessing scalability and resource allocation. |
azure.data.factory.IntegrationRuntimeQueueLength | Count | The number of tasks waiting in the queue for execution within the integration runtime. A high queue length may indicate processing delays or resource constraints. |
azure.data.factory.IntegrationRuntimeAverageTaskPickupDelay | seconds (s) | The average delay before a task is picked up for execution by the integration runtime. Longer delays may suggest resource contention or inefficiencies in task scheduling. |