# Jeremylongshore Claude Code Plugins Plus Skills Databricks Deploy Integration

> Databricks Deploy Integration

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-databricks-d` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-databricks-d`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-databricks-d/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-databricks-d

---

# Databricks Deploy Integration

## Overview
Deploy Databricks jobs, DLT pipelines, and ML models using Declarative Automation Bundles (DABs, formerly Asset Bundles). Bundles provide infrastructure-as-code with `databricks.yml` defining resources, targets (dev/staging/prod), variables, and permissions. The CLI handles validation, deployment, and lifecycle management.

## Prerequisites
- Databricks CLI v0.200+ (`databricks --version`)
- Workspace access with service principal for automated deploys
- `databricks.yml` bundle configuration at project root

## Instructions

### Step 1: Initialize a Bundle
```bash
# Create from a template
databricks bundle init

# Available templates:
# - default-python: Python notebook project
# - default-sql: SQL project
# - mlops-stacks: Full MLOps template with feature engineering
```

### Step 2: Configure `databricks.yml`
```yaml
# databricks.yml — single source of truth for project deployment
bundle:
  name: sales-etl-pipeline

workspace:
  host: ${DATABRICKS_HOST}

variables:
  catalog:
    description: Unity Catalog name
    default: dev_catalog
  alert_email:
    description: Alert notification email
    default: dev@company.com
  warehouse_size:
    default: "2X-Small"

include:
  - resources/*.yml

targets:
  dev:
    default: true
    mode: development
    # dev mode auto-prefixes resources with [username] and enables debug
    workspace:
      root_path: /Users/${workspace.current_user.userName}/.bundle/${bundle.name}/dev
    variables:
      catalog: dev_catalog

  staging:
    workspace:
      root_path: /Shared/.bundle/${bundle.name}/staging
    variables:
      catalog: staging_catalog
      alert_email: staging-alerts@company.com

  prod:
    mode: production
    # production mode prevents accidental destruction
    workspace:
      root_path: /Shared/.bundle/${bundle.name}/prod
    variables:
      catalog: prod_catalog
      alert_email: oncall@company.com
      warehouse_size: "Medium"
```

### Step 3: Define Resources
```yaml
# resources/jobs.yml
resources:
  jobs:
    daily_etl:
      name: "daily-etl-${bundle.target}"
      max_concurrent_runs: 1
      timeout_seconds: 14400

      schedule:
        quartz_cron_expression: "0 0 6 * * ?"
        timezone_id: "UTC"

      email_notifications:
        on_failure: ["${var.alert_email}"]

      tasks:
        - task_key: extract
          notebook_task:
            notebook_path: ./src/extract.py
            base_parameters:
              catalog: "${var.catalog}"
          job_cluster_key: etl

        - task_key: transform
          depends_on: [{task_key: extract}]
          notebook_task:
            notebook_path: ./src/transform.py
          job_cluster_key: etl

        - task_key: load
          depends_on: [{task_key: transform}]
          notebook_task:
            notebook_path: ./src/load.py
          job_cluster_key: etl

      job_clusters:
        - job_cluster_key: etl
          new_cluster:
            spark_version: "14.3.x-scala2.12"
            node_type_id: "i3.xlarge"
            autoscale:
              min_workers: 1
              max_workers: 4
            aws_attributes:
              availability: SPOT_WITH_FALLBACK
              first_on_demand: 1
```

```yaml
# resources/pipelines.yml (DLT)
resources:
  pipelines:
    dlt_pipeline:
      name: "dlt-pipeline-${bundle.target}"
      target: "${var.catalog}.silver"
      catalog: "${var.catalog}"
      libraries:
        - notebook:
            path: ./src/dlt_pipeline.py
      continuous: false
      development: ${bundle.target == "dev"}
```

### Step 4: Deploy Lifecycle Commands
```bash
# Validate — checks YAML syntax, variable resolution, permissions
databricks bundle validate -t staging

# Deploy — creates/updates jobs, uploads notebooks, syncs config
databricks bundle deploy -t staging

# Summary — show what's deployed
databricks bundle summary -t staging

# Run — trigger a specific job/pipeline
databricks bundle run daily_etl -t staging

# Run and wait for completion
databricks bundle run daily_etl -t staging --restart-all-workflows

# Sync — live-reload files during development
databricks bundle sync -t dev --watch

# Destroy — remove all deployed resources (dev only!)
databricks bundle destroy -t dev --auto-approve
```

### Step 5: Promote Staging to Production
```bash
# 1. Validate staging is clean
databricks bundle validate -t staging

# 2. Deploy and test on staging
databricks bundle deploy -t staging
RUN=$(databricks bundle run daily_etl -t staging --output json | jq -r '.run_id')
databricks runs get --run-id $RUN | jq '.state.result_state'

# 3. After staging passes, deploy to production
databricks bundle validate -t prod
databricks bundle deploy -t prod

# 4. Verify production deployment
databricks bundle summary -t prod
databricks jobs list --output json | \
  jq '.[] | select(.settings.name | contains("daily-etl-prod"))'
```

### Step 6: Permissions in Bundles
```yaml
# resources/jobs.yml — add permissions block
resources:
  jobs:
    daily_etl:
      name: "daily-etl-${bundle.target}"
      permissions:
        - group_name: data-engineers
          level: CAN_MANAGE
        - group_name: data-analysts
          level: CAN_VIEW
        - service_principal_name: cicd-service-principal
          level: CAN_MANAGE_RUN
```

## Output
- `databricks.yml` with multi-target deployment (dev/staging/prod)
- Job and pipeline resources defined as code
- Environment-specific variables (catalog, alerts, sizing)
- Promotion workflow from staging to production
- Permissions managed declaratively in bundle config

## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| `bundle validate` fails | Invalid YAML or unresolved variable | Check variable definitions and target config |
| `PERMISSION_DENIED` on deploy | Service principal lacks workspace access | Add SP to workspace in Account Console |
| `RESOURCE_CONFLICT` | Resource name collision across targets | Bundle auto-prefixes in `development` mode |
| `Cluster quota exceeded` | Too many active clusters | Use instance pools or terminate idle clusters |
| `Cannot destroy production` | `mode: production` prevents accidental destroy | This is intentional — remove mode or use `--force` |

## Examples

### Override Variables per Target
```bash
# Override a variable at deploy time
databricks bundle deploy -t prod --var="warehouse_size=Large"
```

### Clean Slate Redeploy (Dev Only)
```bash
databricks bundle destroy -t dev --auto-approve
databricks bundle deploy -t dev
```

## Resources
- [Declarative Automation Bundles](https://docs.databricks.com/aws/en/dev-tools/bundles/)
- [Bundle Configuration Reference](https://docs.databricks.com/aws/en/dev-tools/bundles/reference)
- [Bundle Resources](https://docs.databricks.com/aws/en/dev-tools/bundles/resources)
- [Deployment Modes](https://docs.databricks.com/aws/en/dev-tools/bundles/deployment-modes)

## Next Steps
For multi-environment setup, see `databricks-multi-env-setup`.

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-11 -->