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Blackkadder
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databricks-jobs

Automate Databricks Jobs creation, listing, running, updating, and deletion.

Updated Mar 19, 2026
Search Tags:#automation#orchestration#workflows#databricks#jobs#asset-bundles
One-click install
npx skills add https://github.com/Blackkadder/databricks-apps-and-agents-workshop --skill databricks-jobs-blackkadder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-jobs
Source: https://github.com/Blackkadder/databricks-apps-and-agents-workshop/tree/main/.claude/skills/databricks-jobs
Command: npx skills add https://github.com/Blackkadder/databricks-apps-and-agents-workshop --skill databricks-jobs-blackkadder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the creation, listing, running, updating, and deletion of Databricks Jobs to streamline workflow orchestration and governance.

Core Features & Use Cases

  • Centralized management of job definitions via YAML/Asset Bundles and SDKs.
  • Support for multi-task DAGs, triggers (cron, periodic, file arrivals, table updates), and compute configuration.
  • Real-world scenarios include scheduled ETL pipelines, event-driven processing, and cross-environment deployments with parameterization.

Quick Start

Provide a practical, end-to-end example of creating and managing a Databricks Job using the Skill.

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 Jobs management for scheduled ETL pipelines?▼

You can automate Databricks Jobs management by defining multi-task DAGs, triggers, and compute configurations using the Python SDK, CLI, or Asset Bundles to streamline end-to-end ETL pipeline orchestration.

What is the best way to define Databricks workflows for multi-environment deployments?▼

The best way to define Databricks workflows for multi-environment deployments is using Asset Bundles and YAML frontmatter, enabling centralized job definitions with parameterization across environments.

Can I trigger Databricks pipelines based on file arrivals or table updates?▼

Yes, you can trigger Databricks pipelines through event-driven processing by configuring jobs to activate on file arrivals, table updates, periodic schedules, or cron expressions.

Does this approach support updating and deleting existing Databricks job definitions?▼

Yes, this approach supports the complete job lifecycle, allowing you to list, run, update, and delete existing Databricks job definitions to maintain workflow governance.

Do I need Asset Bundles to orchestrate multi-task DAGs in Databricks?▼

No, you do not need Asset Bundles specifically; you can orchestrate multi-task DAGs using the Python SDK or CLI, while Asset Bundles provide centralized YAML management for definitions.

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