skills.rest
mkgs-databricks-demos
mkgs-databricks-demosOFFICIAL
Explore all skills by this author →
GitHub

databricks-execution-compute

Execute code on Databricks via Connect, Serverless Job, or Interactive Cluster.

Updated Jul 4, 2026
Search Tags:#execution#spark#compute#databricks#ml training
One-click install
npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-execution-compute-mkgs-databricks-demos
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-execution-compute
Source: https://github.com/mkgs-databricks-demos/aiSkillUpdater/tree/main/databricks-execution-compute
Command: npx skills add https://github.com/mkgs-databricks-demos/aiSkillUpdater --skill databricks-execution-compute-mkgs-databricks-demos

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires databricks-cli, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The databricks-execution-compute Skill simplifies running code and managing compute resources on Databricks, allowing users to execute code efficiently through different execution modes.

Core Features & Use Cases

  • Execution Modes: Databricks Connect, Serverless Job, and Interactive Cluster for different use cases.
  • Databricks Connect: Fast, local Spark code execution for data generation and ETL.
  • Serverless Job: Ideal for long-running ML tasks and independent execution without local machine dependency.
  • Interactive Cluster: For preserving state across multiple tool calls and Scala/R support.
  • Use Case: A data scientist might use the Skill to run an ML model training task on a serverless job while maintaining state with an interactive cluster for debugging.

Quick Start

Run a serverless job on Databricks using the databricks-execution-compute skill to execute an ML model training task.

Frequently Asked Questions about databricks-execution-compute

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

FAQPage Schema
How do I execute Spark code and manage compute on Databricks?▼

You can execute Spark code and manage compute on Databricks by using three distinct execution modes: Databricks Connect for fast local execution, Serverless Job for long-running tasks, and Interactive Cluster for stateful execution.

What is the best way to run ML model training tasks on Databricks without relying on my local machine?▼

Running ML model training tasks via a Serverless Job is the best way to execute long-running code independently on Databricks without requiring local machine resources or active local dependencies.

Does Databricks compute execution work with Scala and R, or just Python?▼

Databricks compute execution supports Python, Scala, SQL, and R. To execute Scala and R code specifically, you need to use the Interactive Cluster mode to preserve state across multiple tool calls.

Do I need the Databricks CLI to run code using these execution modes?▼

Yes, you need the Databricks CLI installed and configured to manage compute and run code using Databricks Connect, Serverless Job, and Interactive Cluster execution modes.

When should I use Databricks Connect instead of an Interactive Cluster?▼

Use Databricks Connect for fast, local Spark code execution like data generation and ETL. Use an Interactive Cluster when you need to preserve state across multiple tool calls or run Scala and R code.

Can I run stateful execution across multiple tool calls on Databricks?▼

Yes, you can achieve stateful execution across multiple tool calls on Databricks by utilizing the Interactive Cluster mode, which preserves your environment state between separate code executions.

Related Skills

Explore More Software Engineering Skills→
INNERJOINTINNERJOINT

omc-setup

Install or refresh oh-my-claudecode and CLAUDE.md across plugin, npm, and local-dev environments.

Community
Advanced
patrikboroschpatrikborosch
14

databricks-migration

Port Databricks notebooks, jobs, and Unity Catalog structures to Microsoft Fabric.

Community
Advanced
LaurentPRAT-DBLaurentPRAT-DB

logfood-querier

Query Databricks GTM and consumption data using SQL.

Community
Intermediate
gemini-cli-extensionsgemini-cli-extensions
161

gcp-spark

Coordinate Spark workloads on Google Cloud Dataproc and Serverless.

Official
Advanced