databricks-jobs

name

databricks-jobs

description

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 Lakeflow Jobs

Overview

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.

Reference Files

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

Quick Start

Python SDK

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/[e-mail]/extract",
                source=Source.WORKSPACE
            )
        )
    ]
)
print(f"Created job: {job.job_id}")

CLI

databricks jobs create --json '{
  "name": "my-etl-job",
  "tasks": [{
    "task_key": "extract",
    "notebook_task": {
      "notebook_path": "/Workspace/Users/[e-mail]/extract",
      "source": "WORKSPACE"
    }
  }]
}'

Asset Bundles (DABs)

# resources/jobs.yml
resources:
  jobs:
    my_etl_job:
      name: "[${bundle.target}] My ETL Job"
      tasks:
        - task_key: extract
          notebook_task:
            notebook_path: ../src/notebooks/extract.py

Core Concepts

Multi-Task Workflows

Jobs support DAG-based task dependencies:

tasks:
  - task_key: extract
    notebook_task:
      notebook_path: ../src/extract.py

  - task_key: transform
    depends_on:
      - task_key: extract
    notebook_task:
      notebook_path: ../src/transform.py

  - task_key: load
    depends_on:
      - task_key: transform
    run_if: ALL_SUCCESS  # Only run if all dependencies succeed
    notebook_task:
      notebook_path: ../src/load.py

run_if conditions:

• ALL_SUCCESS (default) - Run when all dependencies succeed
• ALL_DONE - Run when all dependencies complete (success or failure)
• AT_LEAST_ONE_SUCCESS - Run when at least one dependency succeeds
• NONE_FAILED - Run when no dependencies failed
• ALL_FAILED - Run when all dependencies failed
• AT_LEAST_ONE_FAILED - Run when at least one dependency failed

Task Types Summary

Task Type	Use Case	Reference
notebook_task	Run notebooks	task-types.md#notebook-task
spark_python_task	Run Python scripts	task-types.md#spark-python-task
python_wheel_task	Run Python wheels	task-types.md#python-wheel-task
sql_task	Run SQL queries/files	task-types.md#sql-task
dbt_task	Run dbt projects	task-types.md#dbt-task
pipeline_task	Trigger DLT/SDP pipelines	task-types.md#pipeline-task
spark_jar_task	Run Spark JARs	task-types.md#spark-jar-task
run_job_task	Trigger other jobs	task-types.md#run-job-task
for_each_task	Loop over inputs	task-types.md#for-each-task

Trigger Types Summary

Trigger Type	Use Case	Reference
schedule	Cron-based scheduling	triggers-schedules.md#cron-schedule
trigger.periodic	Interval-based	triggers-schedules.md#periodic-trigger
trigger.file_arrival	File arrival events	triggers-schedules.md#file-arrival-trigger
trigger.table_update	Table change events	triggers-schedules.md#table-update-trigger
continuous	Always-running jobs	triggers-schedules.md#continuous-jobs

Compute Configuration

Job Clusters (Recommended)

Define reusable cluster configurations:

job_clusters:
  - job_cluster_key: shared_cluster
    new_cluster:
      spark_version: "15.4.x-scala2.12"
      node_type_id: "i3.xlarge"
      num_workers: 2
      spark_conf:
        spark.speculation: "true"

tasks:
  - task_key: my_task
    job_cluster_key: shared_cluster
    notebook_task:
      notebook_path: ../src/notebook.py

Autoscaling Clusters

new_cluster:
  spark_version: "15.4.x-scala2.12"
  node_type_id: "i3.xlarge"
  autoscale:
    min_workers: 2
    max_workers: 8

Existing Cluster

tasks:
  - task_key: my_task
    existing_cluster_id: "0123-456789-abcdef12"
    notebook_task:
      notebook_path: ../src/notebook.py

Serverless Compute

For notebook and Python tasks, omit cluster configuration to use serverless:

tasks:
  - task_key: serverless_task
    notebook_task:
      notebook_path: ../src/notebook.py
    # No cluster config = serverless

Job Parameters

Define Parameters

parameters:
  - name: env
    default: "dev"
  - name: date
    default: "{{start_date}}"  # Dynamic value reference

Access in Notebook

# In notebook
dbutils.widgets.get("env")
dbutils.widgets.get("date")

Pass to Tasks

tasks:
  - task_key: my_task
    notebook_task:
      notebook_path: ../src/notebook.py
      base_parameters:
        env: "{{job.parameters.env}}"
        custom_param: "value"

Common Operations

Python SDK Operations

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# List jobs
jobs = w.jobs.list()

# Get job details
job = w.jobs.get(job_id=12345)

# Run job now
run = w.jobs.run_now(job_id=12345)

# Run with parameters
run = w.jobs.run_now(
    job_id=12345,
    job_parameters={"env": "prod", "date": "2024-01-15"}
)

# Cancel run
w.jobs.cancel_run(run_id=run.run_id)

# Delete job
w.jobs.delete(job_id=12345)

CLI Operations

# List jobs
databricks jobs list

# Get job details
databricks jobs get 12345

# Run job
databricks jobs run-now 12345

# Run with parameters
databricks jobs run-now 12345 --job-params '{"env": "prod"}'

# Cancel run
databricks jobs cancel-run 67890

# Delete job
databricks jobs delete 12345

Asset Bundle Operations

# Validate configuration
databricks bundle validate

# Deploy job
databricks bundle deploy

# Run job
databricks bundle run my_job_resource_key

# Deploy to specific target
databricks bundle deploy -t prod

# Destroy resources
databricks bundle destroy

Permissions (DABs)

resources:
  jobs:
    my_job:
      name: "My Job"
      permissions:
        - level: CAN_VIEW
          group_name: "data-analysts"
        - level: CAN_MANAGE_RUN
          group_name: "data-engineers"
        - level: CAN_MANAGE
          user_name: "[e-mail]"

Permission levels:

• CAN_VIEW - View job and run history
• CAN_MANAGE_RUN - View, trigger, and cancel runs
• CAN_MANAGE - Full control including edit and delete

Common Issues

Issue	Solution
Job cluster startup slow	Use job clusters with job_cluster_key for reuse across tasks
Task dependencies not working	Verify task_key references match exactly in depends_on
Schedule not triggering	Check pause_status: UNPAUSED and valid timezone
File arrival not detecting	Ensure path has proper permissions and uses cloud storage URL
Table update trigger missing events	Verify Unity Catalog table and proper grants
Parameter not accessible	Use dbutils.widgets.get() in notebooks
"admins" group error	Cannot modify admins permissions on jobs
Serverless task fails	Ensure task type supports serverless (notebook, Python)

Related Skills

• databricks-asset-bundles - Deploy jobs via Databricks Asset Bundles
• databricks-spark-declarative-pipelines - Configure pipelines triggered by jobs

Resources

• Jobs API Reference
• Jobs Documentation
• DABs Job Task Types
• Bundle Examples Repository