agentsclimarketplace

Databricks multi env setup

Skill ComeOnOliver/skillshub/skills/jeremylongshore/claude-code-plugins-plus-skills/databricks-multi-env-setup

🧠 The right skill, one API call. AI agent skills registry with token-efficient skill resolution. 5,000+ skills from 500+ top repos.

Install
npx -y skills add ComeOnOliver/skillshub --skill databricks-multi-env-setup

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Configure Databricks across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Databricks configurations. Trigger with phrases like "databricks environments", "databricks staging", "databricks dev prod", "databricks environment setup", "databricks config by env".

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

8.5 KB, ~2.0k tokens by cl100k_base, as published. Nobody here has run it

Databricks Multi-Environment Setup

Overview

Configure Databricks across dev, staging, and production with isolated workspaces (or catalog-level isolation), per-environment secrets, Asset Bundle targets, and Terraform for workspace provisioning. Each environment gets its own credentials, Unity Catalog namespace, and compute policies.

Prerequisites

  • Databricks account with multiple workspaces (or Premium for catalog-level isolation)
  • Service principals per environment
  • Secret management (Databricks Secret Scopes, AWS Secrets Manager, or GCP Secret Manager)
  • CI/CD pipeline (GitHub Actions, Azure DevOps, etc.)

Environment Strategy

EnvironmentWorkspaceCatalogAuthCompute
DevelopmentShared or dedicateddev_catalogPersonal PATSingle-node, 15min auto-stop
StagingDedicatedstaging_catalogService principalProduction-like, spot instances
ProductionDedicatedprod_catalogService principal (OAuth M2M)Instance pools, auto-scale

Instructions

Step 1: CLI Profiles per Environment

# ~/.databrickscfg
[dev]
host = https://adb-dev-workspace.7.azuredatabricks.net
token = dapi_dev_token

[staging]
host = https://adb-staging-workspace.7.azuredatabricks.net
client_id = staging-sp-client-id
client_secret = staging-sp-secret

[production]
host = https://adb-prod-workspace.7.azuredatabricks.net
client_id = prod-sp-client-id
client_secret = prod-sp-secret
# Use a specific environment
databricks workspace list / --profile staging
databricks clusters list --profile production

Step 2: Asset Bundle Targets

# databricks.yml — single project, multiple targets
bundle:
  name: data-platform

variables:
  catalog:
    description: Unity Catalog for this environment
    default: dev_catalog
  alert_email:
    default: [email protected]
  cluster_size:
    default: "2X-Small"

targets:
  dev:
    default: true
    mode: development
    workspace:
      host: https://adb-dev-workspace.7.azuredatabricks.net
      root_path: /Users/${workspace.current_user.userName}/.bundle/${bundle.name}/dev
    variables:
      catalog: dev_catalog

  staging:
    workspace:
      host: https://adb-staging-workspace.7.azuredatabricks.net
      root_path: /Shared/.bundle/${bundle.name}/staging
    variables:
      catalog: staging_catalog
      alert_email: [email protected]

  prod:
    mode: production
    workspace:
      host: https://adb-prod-workspace.7.azuredatabricks.net
      root_path: /Shared/.bundle/${bundle.name}/prod
    variables:
      catalog: prod_catalog
      alert_email: [email protected]
      cluster_size: "Medium"

Step 3: Per-Environment Secret Scopes

# Create environment-specific secret scopes in each workspace
for env in dev staging prod; do
    databricks secrets create-scope "${env}-secrets" --profile $env
    databricks secrets put-secret "${env}-secrets" db-password --profile $env
    databricks secrets put-secret "${env}-secrets" api-key --profile $env
done
# Access secrets in notebooks — scope name matches environment
import os

env = os.getenv("ENVIRONMENT", "dev")
db_password = dbutils.secrets.get(scope=f"{env}-secrets", key="db-password")
api_key = dbutils.secrets.get(scope=f"{env}-secrets", key="api-key")

Step 4: Environment-Aware Python Config

# config/databricks_config.py
from dataclasses import dataclass
import os

@dataclass
class DatabricksEnvConfig:
    host: str
    catalog: str
    secret_scope: str
    debug: bool
    max_retries: int
    timeout_seconds: int

CONFIGS = {
    "dev": DatabricksEnvConfig(
        host=os.getenv("DATABRICKS_HOST_DEV", ""),
        catalog="dev_catalog",
        secret_scope="dev-secrets",
        debug=True,
        max_retries=3,
        timeout_seconds=30,
    ),
    "staging": DatabricksEnvConfig(
        host=os.getenv("DATABRICKS_HOST_STAGING", ""),
        catalog="staging_catalog",
        secret_scope="staging-secrets",
        debug=False,
        max_retries=3,
        timeout_seconds=60,
    ),
    "prod": DatabricksEnvConfig(
        host=os.getenv("DATABRICKS_HOST_PROD", ""),
        catalog="prod_catalog",
        secret_scope="prod-secrets",
        debug=False,
        max_retries=5,
        timeout_seconds=120,
    ),
}

def get_config() -> DatabricksEnvConfig:
    env = os.getenv("ENVIRONMENT", "dev")
    config = CONFIGS.get(env)
    if not config:
        raise ValueError(f"Unknown environment: {env}")
    if not config.host:
        raise ValueError(f"DATABRICKS_HOST_{env.upper()} not set")
    return config

Step 5: CI/CD with Environment Secrets

# .github/workflows/deploy.yml
name: Deploy Pipeline

on:
  push:
    branches: [main]

jobs:
  deploy-staging:
    runs-on: ubuntu-latest
    environment: staging
    steps:
      - uses: actions/checkout@v4
      - uses: databricks/setup-cli@main
      - run: databricks bundle deploy -t staging
        env:
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST }}
          DATABRICKS_CLIENT_ID: ${{ secrets.DATABRICKS_CLIENT_ID }}
          DATABRICKS_CLIENT_SECRET: ${{ secrets.DATABRICKS_CLIENT_SECRET }}

  deploy-production:
    needs: deploy-staging
    runs-on: ubuntu-latest
    environment: production  # Requires manual approval
    steps:
      - uses: actions/checkout@v4
      - uses: databricks/setup-cli@main
      - run: databricks bundle deploy -t prod
        env:
          DATABRICKS_HOST: ${{ secrets.DATABRICKS_HOST_PROD }}
          DATABRICKS_CLIENT_ID: ${{ secrets.DATABRICKS_CLIENT_ID_PROD }}
          DATABRICKS_CLIENT_SECRET: ${{ secrets.DATABRICKS_CLIENT_SECRET_PROD }}

Step 6: Terraform for Workspace Provisioning (Optional)

# terraform/main.tf
resource "databricks_workspace" "staging" {
  provider                = databricks.accounts
  workspace_name          = "data-platform-staging"
  aws_region             = "us-east-1"
  pricing_tier           = "PREMIUM"
  deployment_name        = "data-platform-staging"
  managed_services_customer_managed_key_id = var.cmk_id
}

resource "databricks_catalog" "staging" {
  provider = databricks.staging
  name     = "staging_catalog"
  comment  = "Staging environment catalog"
}

resource "databricks_schema" "staging_bronze" {
  provider   = databricks.staging
  catalog_name = databricks_catalog.staging.name
  name       = "bronze"
}

Output

  • CLI profiles configured per environment (~/.databrickscfg)
  • Asset Bundle with dev/staging/prod targets and variable overrides
  • Per-environment secret scopes with isolated credentials
  • Python config class for environment-aware code
  • CI/CD pipeline with GitHub environment secrets and approval gates

Error Handling

IssueCauseSolution
Wrong environment targetedMissing --profile or -t flagDefault profile should always be dev
Cross-env data leakShared catalogUse separate catalogs per environment
Secret not foundWrong scope nameVerify scope exists: databricks secrets list-scopes --profile $env
CI auth failureExpired service principal secretRegenerate OAuth secret or use OIDC

Examples

Quick Environment Verification

for profile in dev staging production; do
    echo "=== $profile ==="
    databricks current-user me --profile $profile 2>/dev/null && echo "OK" || echo "FAILED"
done

Startup Validation

config = get_config()
print(f"Environment: {os.getenv('ENVIRONMENT', 'dev')}")
print(f"Catalog: {config.catalog}")
print(f"Debug: {config.debug}")

Resources

Next Steps

For deployment, see databricks-deploy-integration.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.