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databricks-jobs

Automate Databricks Jobs creation, updates, runs, and deletion via CLI and SDK.

27|9|Updated Feb 3, 2026
Search Tags:#automation#CLI#SDK#orchestration#databricks#jobs#asset-bundles
One-click install
npx skills add https://github.com/datasciencemonkey/claude-code-cli-bricks --skill databricks-jobs-datasciencemonkey
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-jobs
Source: https://github.com/datasciencemonkey/claude-code-cli-bricks/tree/main/.claude/skills/databricks-jobs
Command: npx skills add https://github.com/datasciencemonkey/claude-code-cli-bricks --skill databricks-jobs-datasciencemonkey

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables teams to automate and manage Databricks Jobs across creation, listing, execution, updates, and deletion, reducing manual toil and errors.

Core Features & Use Cases

  • Comprehensive Job orchestration via the Databricks Jobs API, CLI, and Asset Bundles.
  • Supports triggers, schedules, notifications, health checks, and cross-job orchestration.
  • Real-world use case: define a multi-step ETL pipeline, monitor runs, and automatically re-run failed steps or trigger downstream jobs.

Quick Start

To get started, connect to your Databricks workspace and use the Databricks CLI or Python SDK to create and schedule a simple notebook job. Install the Databricks CLI and SDK if you haven't, configure your workspace, and run a basic job to verify setup.

Frequently Asked Questions about databricks-jobs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate Databricks job orchestration using CLI and SDK?▼

Automate Databricks job orchestration by using the CLI, Python SDK, and Asset Bundles to create, list, run, update, or delete jobs, triggers, schedules, and notifications for reliable automated workflows.

Can I define multi-step ETL pipelines in Databricks with YAML-based configurations?▼

Yes, you can define multi-step ETL pipelines using YAML-based configurations and Asset Bundles to orchestrate notebook jobs, pass parameters, and monitor run health across your Databricks workspace.

What is the best way to monitor Databricks jobs and handle failures?▼

The best way to monitor Databricks jobs is by configuring health checks and notifications to monitor runs, automatically re-run failed steps, and trigger downstream jobs for cross-job orchestration.

Do I need the Databricks CLI installed to schedule and run jobs?▼

Yes, you need the Databricks CLI and Python SDK installed and your workspace configured to create, schedule, and run jobs, ensuring reliable automated job workflows without manual toil.

Does Databricks job orchestration support cross-job triggers and notifications?▼

Yes, Databricks job orchestration supports triggers, schedules, and notifications, allowing you to automatically trigger downstream jobs and re-run failed steps based on health monitoring.

How do I update or delete existing Databricks jobs without manual intervention?▼

Update or delete existing Databricks jobs without manual intervention by leveraging the Databricks Jobs API, CLI, or SDK to apply changes to triggers, schedules, and job configurations programmatically.

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