Use this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job' or'trigger job',(4) 'job status' or 'check job', (5) scheduling with cron or triggers, (6) configuring notifications/monitoring, (7) ANY task involving Databricks Jobs via CLI, Python SDK, or Asset Bundles. ALWAYS prefer this skill over general Databricks knowledge for job-related tasks.
Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles.
| Use Case | Reference File |
|---|---|
| Configure task types (notebook, Python, SQL, dbt, etc.) | task-types.md |
| Set up triggers and schedules | triggers-schedules.md |
| Configure notifications and health monitoring | notifications-monitoring.md |
| Complete working examples | examples.md |
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import Task, NotebookTask, Source
w = WorkspaceClient()
job = w.jobs.create(
name="my-etl-job",
tasks=[
Task(
task_key="extract",
notebook_task=NotebookTask(
notebook_path="/Workspace/Users/[email protected]/extract",
source=Source.WORKSPACE
)
)
]
)
print(f"Created job: {job.job_id}")
databricks jobs create --json '{
"name": "my-etl-job",
"tasks": [{
"task_key": "extract",
"notebook_task": {
"notebook_path": "/Workspace/Users/[email protected]/extract",
"source": "WORKSPACE"
}
}]
}'
# resources/jobs.yml