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databricks-cost-optimizer

Analyze Databricks system.billing usage data to identify cost drivers and savings opportunities.

Updated Feb 7, 2026
Search Tags:#cost-optimization#cloud-spend#dbu#databricks-cost-optimizer#compute-right-sizing#billing-analysis
One-click install
npx skills add https://github.com/rywerth-dbx/claude-databricks-starter --skill databricks-cost-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: databricks-cost-optimizer
Source: https://github.com/rywerth-dbx/claude-databricks-starter/tree/main/.claude/skills/databricks-cost-optimizer
Command: npx skills add https://github.com/rywerth-dbx/claude-databricks-starter --skill databricks-cost-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data teams reduce Databricks spend by analyzing usage and billing data to identify efficiency opportunities and misallocated resources across clusters, jobs, and warehouses.

Core Features & Use Cases

  • Spend overview and driver-level cost attribution to target the largest cost centers.
  • Idle/over-provisioned cluster detection and recommendations to right-size or auto-terminate.
  • Job, SQL warehouse, and streaming workload optimization with actionable migration paths and cost-saving steps.

Quick Start

Run the cost optimization analysis on your workspace to surface the highest ROI opportunities.

Frequently Asked Questions about databricks-cost-optimizer

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

FAQPage Schema
How do I find idle clusters and reduce Databricks cloud spend?▼

Databricks cost optimization works by analyzing system.billing.usage and system.compute data to attribute spend to specific drivers. It surfaces high-cost workloads, idle resources, and misconfigurations across clusters, jobs, and SQL warehouses.

What Databricks system tables are required to analyze billing usage?▼

Analyzing Databricks billing usage requires access to system.billing.usage, system.billing.list_prices, and system.compute.* tables. Querying these system tables provides the raw data needed to derive actionable cost-saving targets.

How do I right-size compute resources for Databricks jobs and SQL warehouses?▼

You can right-size compute for Databricks jobs and SQL warehouses by analyzing usage patterns to identify over-provisioned resources. The analysis provides actionable migration paths and specific cost-saving steps to optimize workloads.

Can I get cost attribution for individual Databricks workloads and jobs?▼

Yes, you can get driver-level cost attribution for individual Databricks workloads by analyzing billing usage data. This attribution targets the largest cost centers across clusters, jobs, and streaming workloads to prioritize savings.

What is the best way to identify high-cost Databricks workloads for optimization?▼

The best way to identify high-cost Databricks workloads is to run a cost optimization analysis across your workspace. This evaluates system billing data to surface the highest ROI opportunities, including misconfigured clusters and inefficient jobs.

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