databricks-serverless-migration
SkillMigrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concre
Install
git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-serverless-migrationWhat is databricks-serverless-migration?
Migrate Databricks workloads from classic compute to serverless compute. Use when migrating notebooks, jobs, pipelines, or Scala JARs (`spark_jar_task`) from classic clusters to serverless, checking if existing code is serverless-compatible, or writing new serverless-compatible code. Provides concrete fixes for the serverless Spark Connect architecture and guides the full migration. Not for classic DBR version upgrades or cluster configuration changes within classic compute.
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Inherit all session tools
Declares no tool restrictions — inherits every session tool
~120 tokens of context used while enabled, before you invoke anything
Documentation
README · ~37 min readServerless Compute Migration
FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.
Analyze existing Databricks code for serverless compute compatibility and guide migration from classic clusters. The skill follows a 4-step migration lifecycle: Ingest the workload → Analyze for compatibility → Test via A/B comparison → Validate and iterate.
When to Use This Skill
- Migrating notebooks, jobs, or pipelines from classic compute to serverless
- Checking if existing code is serverless-compatible
- Writing new code that targets serverless compute
- Troubleshooting serverless-specific errors after migration
- Choosing between Performance-Optimized and Standard mode
Where to Run This Skill
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