databricks-multi-env-setup
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".
Best use case
databricks-multi-env-setup is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using databricks-multi-env-setup should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/databricks-multi-env-setup/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How databricks-multi-env-setup Compares
| Feature / Agent | databricks-multi-env-setup | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
| Environment | Workspace | Catalog | Auth | Compute |
|-------------|-----------|---------|------|---------|
| Development | Shared or dedicated | `dev_catalog` | Personal PAT | Single-node, 15min auto-stop |
| Staging | Dedicated | `staging_catalog` | Service principal | Production-like, spot instances |
| Production | Dedicated | `prod_catalog` | Service principal (OAuth M2M) | Instance pools, auto-scale |
## Instructions
### Step 1: CLI Profiles per Environment
```ini
# ~/.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
```
```bash
# Use a specific environment
databricks workspace list / --profile staging
databricks clusters list --profile production
```
### Step 2: Asset Bundle Targets
```yaml
# databricks.yml — single project, multiple targets
bundle:
name: data-platform
variables:
catalog:
description: Unity Catalog for this environment
default: dev_catalog
alert_email:
default: dev@company.com
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/${bundle.name}/staging
variables:
catalog: staging_catalog
alert_email: staging-alerts@company.com
prod:
mode: production
workspace:
host: https://adb-prod-workspace.7.azuredatabricks.net
root_path: /Shared/.bundle/${bundle.name}/prod
variables:
catalog: prod_catalog
alert_email: oncall@company.com
cluster_size: "Medium"
```
### Step 3: Per-Environment Secret Scopes
```bash
# 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
```
```python
# 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
```python
# 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
```yaml
# .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)
```hcl
# 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
| Issue | Cause | Solution |
|-------|-------|----------|
| Wrong environment targeted | Missing `--profile` or `-t` flag | Default profile should always be dev |
| Cross-env data leak | Shared catalog | Use separate catalogs per environment |
| Secret not found | Wrong scope name | Verify scope exists: `databricks secrets list-scopes --profile $env` |
| CI auth failure | Expired service principal secret | Regenerate OAuth secret or use OIDC |
## Examples
### Quick Environment Verification
```bash
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
```python
config = get_config()
print(f"Environment: {os.getenv('ENVIRONMENT', 'dev')}")
print(f"Catalog: {config.catalog}")
print(f"Debug: {config.debug}")
```
## Resources
- [Declarative Automation Bundles](https://docs.databricks.com/aws/en/dev-tools/bundles/)
- [CLI Authentication](https://docs.databricks.com/aws/en/dev-tools/cli/authentication)
- [Terraform Provider](https://docs.databricks.com/aws/en/dev-tools/terraform/)
## Next Steps
For deployment, see `databricks-deploy-integration`.Related Skills
websocket-handler-setup
Websocket Handler Setup - Auto-activating skill for Backend Development. Triggers on: websocket handler setup, websocket handler setup Part of the Backend Development skill category.
vpc-network-setup
Vpc Network Setup - Auto-activating skill for GCP Skills. Triggers on: vpc network setup, vpc network setup Part of the GCP Skills skill category.
tensorflow-serving-setup
Tensorflow Serving Setup - Auto-activating skill for ML Deployment. Triggers on: tensorflow serving setup, tensorflow serving setup Part of the ML Deployment skill category.
sqs-queue-setup
Sqs Queue Setup - Auto-activating skill for AWS Skills. Triggers on: sqs queue setup, sqs queue setup Part of the AWS Skills skill category.
spy-setup-helper
Spy Setup Helper - Auto-activating skill for Test Automation. Triggers on: spy setup helper, spy setup helper Part of the Test Automation skill category.
spike-test-setup
Spike Test Setup - Auto-activating skill for Performance Testing. Triggers on: spike test setup, spike test setup Part of the Performance Testing skill category.
sla-monitor-setup
Sla Monitor Setup - Auto-activating skill for Enterprise Workflows. Triggers on: sla monitor setup, sla monitor setup Part of the Enterprise Workflows skill category.
server-sent-events-setup
Server Sent Events Setup - Auto-activating skill for API Integration. Triggers on: server sent events setup, server sent events setup Part of the API Integration skill category.
react-context-setup
React Context Setup - Auto-activating skill for Frontend Development. Triggers on: react context setup, react context setup Part of the Frontend Development skill category.
rds-instance-setup
Rds Instance Setup - Auto-activating skill for AWS Skills. Triggers on: rds instance setup, rds instance setup Part of the AWS Skills skill category.
rabbitmq-queue-setup
Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.
pre-commit-hook-setup
Pre Commit Hook Setup - Auto-activating skill for DevOps Basics. Triggers on: pre commit hook setup, pre commit hook setup Part of the DevOps Basics skill category.