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)

# Verify infrastructure
databricks clusters list-node-types --output json | jq '.[0:5] | .[].node_type_id'
databricks instance-pools list --output json | jq '.[] | {id: .instance_pool_id, name: .instance_pool_name}'

Step 3: Code Quality Gates

• Unit tests passing locally (pytest tests/unit/)
• Integration tests passing on staging data
• No .collect() on large datasets
• No hardcoded credentials or paths
• Error handling covers all failure modes
• Delta Lake best practices: MERGE for upserts, OPTIMIZE scheduled
• Logging is production-appropriate (structured, no PII)

# Run tests and validate bundle
pytest tests/ -v --tb=short
databricks bundle validate -t prod

Step 4: Job Configuration

# resources/prod_etl.yml
resources:
  jobs:
    prod_etl_pipeline:
      name: "prod-etl-pipeline"
      tags:
        environment: production
        team: data-engineering
        cost_center: analytics

      schedule:
        quartz_cron_expression: "0 0 6 * * ?"
        timezone_id: "America/New_York"

      email_notifications:
        on_failure: ["oncall@company.com"]
        on_success: ["data-team@company.com"]

      webhook_notifications:
        on_failure:
          - id: "slack-notification-destination-id"

      max_concurrent_runs: 1
      timeout_seconds: 14400  # 4 hours

      tasks:
        - task_key: bronze_ingest
          job_cluster_key: etl_cluster
          notebook_task:
            notebook_path: src/pipelines/bronze.py
          timeout_seconds: 3600

        - task_key: silver_transform
          depends_on: [{task_key: bronze_ingest}]
          job_cluster_key: etl_cluster
          notebook_task:
            notebook_path: src/pipelines/silver.py

        - task_key: gold_aggregate
          depends_on: [{task_key: silver_transform}]
          job_cluster_key: etl_cluster
          notebook_task:
            notebook_path: src/pipelines/gold.py

      job_clusters:
        - job_cluster_key: etl_cluster
          new_cluster:
            spark_version: "14.3.x-scala2.12"
            node_type_id: "i3.xlarge"
            autoscale:
              min_workers: 2
              max_workers: 8
            spark_conf:
              spark.sql.shuffle.partitions: "200"
              spark.databricks.delta.optimizeWrite.enabled: "true"
              spark.databricks.delta.autoCompact.enabled: "true"
            aws_attributes:
              availability: SPOT_WITH_FALLBACK
              first_on_demand: 1

Step 5: Deploy

# Pre-flight checks
echo "=== Pre-flight ==="
databricks bundle validate -t prod
databricks workspace list /Shared/.bundle/ 2>/dev/null || echo "First deploy"
databricks secrets list-scopes | grep prod

# Deploy
echo "=== Deploying ==="
databricks bundle deploy -t prod

# Verify deployment
databricks bundle summary -t prod

# Trigger verification run
echo "=== Verification ==="
RUN_ID=$(databricks bundle run prod_etl_pipeline -t prod --output json | jq -r '.run_id')
echo "Verification run: $RUN_ID"

# Wait and check result
databricks runs get --run-id $RUN_ID --output json | jq '.state'

Step 6: Post-Deploy Monitoring

from databricks.sdk import WorkspaceClient
from datetime import datetime

w = WorkspaceClient()

def check_job_health(job_id: int) -> dict:
    """Post-deploy health check."""
    runs = list(w.jobs.list_runs(job_id=job_id, completed_only=True, limit=10))
    if not runs:
        return {"status": "NO_RUNS", "healthy": False}

    successful = sum(1 for r in runs if r.state.result_state.value == "SUCCESS")
    success_rate = successful / len(runs)

    durations = [
        (r.end_time - r.start_time) / 60000
        for r in runs if r.end_time and r.start_time
    ]
    avg_duration = sum(durations) / len(durations) if durations else 0

    return {
        "healthy": success_rate > 0.9 and runs[0].state.result_state.value == "SUCCESS",
        "success_rate": f"{success_rate:.0%}",
        "avg_duration_min": f"{avg_duration:.1f}",
        "last_run": runs[0].state.result_state.value,
        "last_run_time": datetime.fromtimestamp(runs[0].start_time / 1000).isoformat(),
    }

Step 7: Rollback Procedure

#!/bin/bash
set -euo pipefail
# rollback.sh <job_id>

JOB_ID=$1
echo "=== ROLLBACK: Job $JOB_ID ==="

# 1. Pause the schedule
echo "Pausing schedule..."
databricks jobs update --job-id $JOB_ID --json '{"settings": {"schedule": null}}'

# 2. Cancel any active runs
echo "Cancelling active runs..."
databricks runs list --job-id $JOB_ID --active-only --output json | \
  jq -r '.runs[]?.run_id' | \
  xargs -I {} databricks runs cancel --run-id {}

# 3. Redeploy previous bundle version
echo "Redeploying previous version..."
git checkout HEAD~1 -- resources/ src/
databricks bundle deploy -t prod

# 4. Restore schedule
echo "Re-enabling schedule..."
databricks jobs reset --job-id $JOB_ID --json-file resources/prod_etl.json

# 5. Trigger verification
echo "Running verification..."
databricks jobs run-now --job-id $JOB_ID

echo "=== ROLLBACK COMPLETE ==="

Output

• Pre-deployment checklist verified
• Production job deployed via Asset Bundles
• Verification run completed successfully
• Monitoring health check operational
• Rollback procedure documented and tested

Error Handling

Alert	Condition	Severity	Action
Job Failed	result_state = FAILED	P1	Page oncall, check get_run_output
Long Running	Duration > 2x average	P2	Investigate cluster sizing
3+ Consecutive Failures	Success rate drops below 70%	P1	Trigger rollback
Data Quality Failed	DLT expectations failed	P2	Check source data quality

Examples

Production Health Dashboard

SELECT job_id, job_name,
       COUNT(*) AS total_runs,
       SUM(CASE WHEN result_state = 'SUCCESS' THEN 1 ELSE 0 END) 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() - INTERVAL 7 DAYS
GROUP BY job_id, job_name
ORDER BY total_runs DESC;

Resources

• Deployment Modes
• Job Configuration
• Cluster Best Practices

Next Steps

For version upgrades, see databricks-upgrade-migration.