What causes Fabric Pipeline Error 3203?

The interactive Databricks cluster was auto-terminated due to inactivity before the pipeline job could be submitted. A race condition occurred where the cluster began shutting down as the job was being dispatched. The cluster was manually stopped or terminated by an administrator. Cluster configuration uses interactive clusters with aggressive auto-termination settings

How do I fix Fabric Pipeline Error 3203?

Step 1: Navigate to your Databricks workspace and check the state of the target cluster; restart it if it is in a Terminated state.. Step 2: Switch from an interactive cluster to a job cluster in your Fabric pipeline's Databricks linked service configuration to avoid auto-termination race conditions.. Step 3: If you must use an interactive cluster, increase the auto-termination timeout to reduce the likelihood of termination between pipeline trigger and job submission.. Step 4: Add a retry policy to the Databricks activity in your Fabric pipeline (e.g., 2–3 retries with a 60-second interval) to handle transient terminated-state errors.. Step 5: Monitor cluster availability in Databricks before pipeline execution using a pipeline validation step or pre-check activity.

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Power BI Refresh Error:
Fabric Pipeline Error 3203

What does this error mean?

The Databricks cluster targeted by the pipeline is in a Terminated state and cannot accept new jobs. This often occurs as a race condition with interactive clusters.

Common causes

1The interactive Databricks cluster was auto-terminated due to inactivity before the pipeline job could be submitted
2A race condition occurred where the cluster began shutting down as the job was being dispatched
3The cluster was manually stopped or terminated by an administrator
4Cluster configuration uses interactive clusters with aggressive auto-termination settings

How to fix it

1Step 1: Navigate to your Databricks workspace and check the state of the target cluster; restart it if it is in a Terminated state.
2Step 2: Switch from an interactive cluster to a job cluster in your Fabric pipeline's Databricks linked service configuration to avoid auto-termination race conditions.
3Step 3: If you must use an interactive cluster, increase the auto-termination timeout to reduce the likelihood of termination between pipeline trigger and job submission.
4Step 4: Add a retry policy to the Databricks activity in your Fabric pipeline (e.g., 2–3 retries with a 60-second interval) to handle transient terminated-state errors.
5Step 5: Monitor cluster availability in Databricks before pipeline execution using a pipeline validation step or pre-check activity.

Frequently asked questions

Why does this error happen more often with interactive clusters than job clusters?

Interactive clusters have auto-termination policies that can shut them down during periods of inactivity. Job clusters are spun up on demand for each job and torn down afterward, eliminating the risk of being in a Terminated state when a pipeline tries to submit a job.

Will adding retries to the pipeline activity fix this error permanently?

Retries can resolve transient occurrences caused by race conditions, but they are not a permanent fix. The recommended long-term solution is to migrate to job clusters, which are purpose-built for automated pipeline workloads.