Lakeflow Jobs Development

FIRST: Use the parent databricks-core skill for CLI basics, authentication, profile selection, and data exploration commands.

Lakeflow Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Asset Bundles (DABs), Python SDK, or CLI.

Reference Files

Scaffolding a New Job Project

Use databricks bundle init with a config file to scaffold non-interactively. This creates a project in the <project_name>/ directory:

databricks bundle init default-python --config-file <(echo '{"project_name": "my_job", "include_job": "yes", "include_pipeline": "no", "include_python": "yes", "serverless": "yes"}') --profile <PROFILE> < /dev/null

• project_name: letters, numbers, underscores only

After scaffolding, create CLAUDE.md and AGENTS.md in the project directory. These files are essential to provide agents with guidance on how to work with the project. Use this content:

# Declarative Automation Bundles Project

This project uses Declarative Automation Bundles (formerly Databricks Asset Bundles) for deployment.

## Prerequisites

Install the Databricks CLI (>= v0.288.0) if not already installed:
- macOS: `brew tap databricks/tap && brew install databricks`
- Linux: `curl -fsSL https://raw.githubusercontent.com/databricks/setup-cli/main/install.sh | sh`
- Windows: `winget install Databricks.DatabricksCLI`

Verify: `databricks -v`

## For AI Agents

Read the `databricks-core` skill for CLI basics, authentication, and deployment workflow.
Read the `databricks-jobs` skill for job-specific guidance.

If skills are not available, install them: `databricks aitools install`

Project Structure

my-job-project/
├── databricks.yml              # Bundle configuration
├── resources/
│   └── my_job.job.yml          # Job definition
├── src/
│   ├── my_notebook.ipynb       # Notebook tasks
│   └── my_module/              # Python wheel package
│       ├── __init__.py
│       └── main.py
├── tests/
│   └── test_main.py
└── pyproject.toml              # Python project config (if using wheels)

Quick Start

Asset Bundles (DABs) — recommended

# 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

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/Shared/etl/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/Shared/etl/extract",
      "source": "WORKSPACE"
    }
  }]
}'

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

Trigger Types Summary

Compute Configuration

Job Clusters (recommended)

Define reusable cluster configurations shared across tasks:

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

Parameters defined at job level are passed to ALL tasks (no need to repeat per task):

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

Access in notebooks:

catalog = dbutils.widgets.get("env")
load_date = dbutils.widgets.get("date")

Pass to specific 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

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

# List jobs
databricks jobs list

# Get job details
databricks jobs get 12345

# Run job
databricks jobs run-now 12345

# Run with parameters (must use --json with job_id inside)
databricks jobs run-now --json '{"job_id": 12345, "job_parameters": {"env": "prod"}}'

# Cancel run
databricks jobs cancel-run 67890

# Delete job
databricks jobs delete 12345

Asset Bundle Operations

# Validate configuration
databricks bundle validate --profile <profile>

# Deploy to a target
databricks bundle deploy -t dev --profile <profile>

# Run a job
databricks bundle run <job_name> -t dev --profile <profile>

# Check run status
databricks jobs get-run --run-id <id> --profile <profile>

# Destroy resources
databricks bundle destroy --auto-approve

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: "admin@example.com"

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

Unit Testing

Run unit tests locally:

uv run pytest

Development Workflow

1. Validate: databricks bundle validate --profile <profile>
2. Deploy: databricks bundle deploy -t dev --profile <profile>
3. Run: databricks bundle run <job_name> -t dev --profile <profile>
4. Check run status: databricks jobs get-run --run-id <id> --profile <profile>

Common Issues

Related Skills

• databricks-dabs — DABs configuration patterns shared by jobs and pipelines
• databricks-pipelines — SDP (formerly DLT) pipelines triggered by pipeline_task

Documentation

• Lakeflow Jobs
• Task types
• Declarative Automation Bundles
• Jobs API Reference
• Bundle Examples Repository