Databricks Jobs

Lakeflow Jobs Development

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

Lakeflow Jobs are scheduled workflows that run notebooks, Python scripts, SQL queries, and other tasks on Databricks.

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 experimental aitools skills 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)

Configuring Tasks

Edit resources/<job_name>.job.yml to configure tasks:

resources:
  jobs:
    my_job:
      name: my_job

      tasks:
        - task_key: my_notebook
          notebook_task:
            notebook_path: ../src/my_notebook.ipynb

        - task_key: my_python
          depends_on:
            - task_key: my_notebook
          python_wheel_task:
            package_name: my_package
            entry_point: main

Task types: notebook_task, python_wheel_task, spark_python_task, pipeline_task, sql_task

Job Parameters

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

resources:
  jobs:
    my_job:
      parameters:
        - name: catalog
          default: ${var.catalog}
        - name: schema
          default: ${var.schema}

Access parameters in notebooks with dbutils.widgets.get("catalog").

Writing Notebook Code

# Read parameters
catalog = dbutils.widgets.get("catalog")
schema = dbutils.widgets.get("schema")

# Read tables
df = spark.read.table(f"{catalog}.{schema}.my_table")

# SQL queries
result = spark.sql(f"SELECT * FROM {catalog}.{schema}.my_table LIMIT 10")

# Write output
df.write.mode("overwrite").saveAsTable(f"{catalog}.{schema}.output_table")

Scheduling

resources:
  jobs:
    my_job:
      trigger:
        periodic:
          interval: 1
          unit: DAYS

Or with cron:

schedule:
  quartz_cron_expression: "0 0 2 * * ?"
  timezone_id: "UTC"

Multi-Task Jobs with Dependencies

resources:
  jobs:
    my_pipeline_job:
      tasks:
        - task_key: extract
          notebook_task:
            notebook_path: ../src/extract.ipynb

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

        - task_key: load
          depends_on:
            - task_key: transform
          notebook_task:
            notebook_path: ../src/load.ipynb

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>

Documentation

• Lakeflow Jobs: https://docs.databricks.com/jobs
• Task types: https://docs.databricks.com/jobs/configure-task
• Declarative Automation Bundles: https://docs.databricks.com/dev-tools/bundles/