Execute Databricks production deployment checklist and rollback procedures.
Use when deploying Databricks jobs to production, preparing for launch,
or implementing go-live procedures.
Trigger with phrases like "databricks production", "deploy databricks",
"databricks go-live", "databricks launch checklist".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Execute Databricks production deployment checklist and rollback procedures.
Use when deploying Databricks jobs to production, preparing for launch,
or implementing go-live procedures.
Trigger with phrases like "databricks production", "deploy databricks",
"databricks go-live", "databricks launch checklist".
allowed-tools
Read, Bash(databricks:*), Grep
version
1.0.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
compatible-with
claude-code, codex, openclaw
tags
["saas","databricks","deployment"]
Databricks Production Checklist
Overview
Complete checklist for deploying Databricks jobs and pipelines to production. Covers security hardening, infrastructure validation, code quality gates, job configuration, deployment commands, monitoring setup, and rollback procedures.
Prerequisites
Staging environment tested and verified
Production workspace access with service principal
Unity Catalog configured with prod catalogs
Monitoring and alerting ready (see databricks-observability)
Instructions
Step 1: Pre-Deployment Security
Service principal configured for automated runs (not personal PAT)
Secrets in Databricks Secret Scopes (not env vars or hardcoded)
Token expiration set (max 90 days)
Unity Catalog grants follow least privilege
Cluster policies enforced for cost/security guardrails
IP access lists configured in Admin Console
Audit logging verified via system.access.audit
Step 2: Infrastructure Validation
Instance pool created for fast cluster startup
Node types validated for workload (compute-optimized for streaming, memory-optimized for ML)
Autoscaling configured with sensible min/max workers
Spot instances enabled for worker nodes (on-demand for driver)
Auto-termination disabled for job clusters (they terminate on completion)
SELECT job_id, job_name,
COUNT(*) AS total_runs,
SUM(CASEWHEN result_state ='SUCCESS'THEN1ELSE0END) AS successes,
ROUND(AVG(execution_duration) /60000, 1) AS avg_minutes,
MAX(start_time) AS last_run
FROM system.lakeflow.job_run_timeline
WHERE start_time >current_timestamp() -INTERVAL7 DAYS
GROUPBY job_id, job_name
ORDERBY total_runs DESC;