Databricks Asset Bundle Deployment

Package tested code into Databricks Asset Bundles (DABs) and deploy to multiple environments (dev/staging/prod) with proper parameterization and governance.

When to Use This Skill

• Packaging tested code for deployment (after databricks-testing)
• Creating production-ready pipeline projects
• Deploying to dev/staging/prod environments
• Setting up multi-environment CI/CD
• Managing notebook deployments
• Scheduling jobs in Databricks

Core Concepts

Databricks Asset Bundles (DABs)

DABs are the standard way to package and deploy Databricks workflows:

• Infrastructure as code for Databricks
• Version control friendly (Git)
• Multi-environment support (dev/staging/prod)
• Automated validation and deployment
• Consistent project structure

Two-Phase Workflow

Phase 1: Test & Iterate (using databricks-testing skill)

• Test code on cluster via MCP
• Debug and fix errors
• Iterate until working

Phase 2: Package & Deploy (this skill)

• Create DAB project structure
• Generate databricks.yml and job definitions
• Validate bundle
• Deploy to environment
• (Optional) Run deployed job

Standard Project Structure

project_name/
├── databricks.yml              # Bundle configuration (REQUIRED)
├── resources/                  # Job/pipeline definitions (REQUIRED)
│   └── job.yml                # Job definition
├── src/                       # Source code (RECOMMENDED)
│   └── project_name/
│       └── notebooks/
│           ├── 01_data_prep.py
│           ├── 02_transform.py
│           └── 03_output.py
└── tests/                     # Unit tests (OPTIONAL)
    └── test_transformations.py

Key Files

databricks.yml - Bundle configuration:

• Bundle name and variables
• Environment targets (dev/staging/prod)
• References to resources

resources/*.yml - Job/pipeline definitions:

• Task configurations
• Cluster settings (use serverless)
• Schedules and triggers
• Notebook paths and parameters

Deployment Workflows

Workflow 1: Create Bundle from Scratch

Package working code into new DAB project.

Pattern:

1. Create project directory structure
2. Generate databricks.yml with:
   • Bundle name
   • Variables (catalog, schema, etc.)
   • Targets (dev, staging, prod)
3. Create job definition in resources/job.yml
4. Move tested notebooks to src/<project>/notebooks/
5. Add parameterization (widgets) to notebooks
6. Validate (automatic, no confirmation)
7. Deploy (automatic, no confirmation)
8. Ask before running (requires user confirmation)

Workflow 2: Validate and Deploy (AUTOMATIC)

After bundle creation, automatically validate and deploy.

Pattern:

# Step 1: Validate (AUTOMATIC - no confirmation needed)
databricks bundle validate -t dev

# If validation fails:
# - Show errors
# - Fix databricks.yml or resource files
# - Re-run validate

# Step 2: Deploy (AUTOMATIC - no confirmation needed)
databricks bundle deploy -t dev

# Reports:
# - Deployment success
# - Job name and ID
# - Workspace URL

IMPORTANT: These commands run automatically per CLAUDE.md rules.

Workflow 3: Run Deployed Job (REQUIRES CONFIRMATION)

Execute the deployed job.

Pattern:

# IMPORTANT: ALWAYS ask user first
# "Do you want to run the deployed job '<job_name>' now?"

# Only if user confirms:
databricks bundle run <job_name> -t dev

# Monitor and report:
# - Run URL
# - Run status (RUNNING, SUCCESS, FAILED)
# - Result state
# - Error messages if failed

IMPORTANT: Never run jobs without explicit user confirmation per CLAUDE.md rules.

Parameterization

Required Parameterization Patterns

Never hard-code values. Always use variables.

Bundle Variables (databricks.yml):

variables:
  catalog:
    description: "Unity Catalog name"
    default: "dev_catalog"

  schema:
    description: "Schema name"
    default: "default"

  project_name:
    description: "Project identifier"

Environment-Specific Values (targets):

targets:
  dev:
    mode: development
    variables:
      catalog: "dev_catalog"
      schema: "dev_schema"

  prod:
    mode: production
    variables:
      catalog: "prod_catalog"
      schema: "prod_schema"

Built-in Variables:

• ${var.catalog} - User-defined variable
• ${bundle.target} - Current environment (dev/staging/prod)
• ${workspace.current_user.userName} - Current user email
• ${workspace.file_path} - Workspace file path

Notebook Widget Parameterization

All notebooks must use widgets with defaults:

# REQUIRED pattern for all notebook parameters
try:
    catalog = dbutils.widgets.get("catalog")
except:
    catalog = "dev_catalog"

try:
    schema = dbutils.widgets.get("schema")
except:
    schema = "default"

try:
    batch_date = dbutils.widgets.get("batch_date")
except:
    from datetime import date
    batch_date = str(date.today())

Why try/except:

• Allows local testing without widgets
• Provides sensible defaults
• Prevents errors in interactive mode

Serverless Compute Guidelines

DO:

• Rely on serverless compute (no new_cluster in tasks)
• Use %pip install for Python dependencies
• Keep tasks small and focused
• Use Delta Lake for data persistence

DON'T:

• Define new_cluster in task configuration
• Install libraries via cluster init scripts
• Run long operations without checkpoints
• Use non-Delta formats for production data

Example Task Configuration:

tasks:
  - task_key: data_prep
    notebook_task:
      notebook_path: ../src/project/notebooks/01_prep.py
      base_parameters:
        catalog: ${var.catalog}
    # NO new_cluster here - uses serverless by default

Path Resolution Rules

CRITICAL: Paths in resources/*.yml resolve relative to the resource file.

project/
├── databricks.yml
├── resources/
│   └── job.yml          # Paths resolve from HERE
└── src/
    └── notebooks/
        └── notebook.py

In resources/job.yml:

notebook_path: ../src/notebooks/notebook.py  # Relative to resources/

Not:

notebook_path: src/notebooks/notebook.py  # Wrong - from project root

Complete Bundle Examples

Example 1: Simple Data Pipeline Bundle

databricks.yml:

bundle:
  name: ${var.project_name}

variables:
  project_name:
    description: "Project identifier"
    default: "my_pipeline"

  catalog:
    description: "Unity Catalog name"
    default: "dev_catalog"

  schema:
    description: "Schema name"
    default: "pipeline_data"

targets:
  dev:
    mode: development
    workspace:
      host: ${DATABRICKS_HOST}
    variables:
      catalog: "dev_catalog"

  prod:
    mode: production
    workspace:
      host: ${DATABRICKS_HOST}
    variables:
      catalog: "prod_catalog"

resources:
  jobs:
    my_pipeline_job:
      name: ${var.project_name}_job_${bundle.target}

      tasks:
        - task_key: data_ingestion
          notebook_task:
            notebook_path: ../src/${var.project_name}/notebooks/01_ingest.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

        - task_key: data_transformation
          depends_on:
            - task_key: data_ingestion
          notebook_task:
            notebook_path: ../src/${var.project_name}/notebooks/02_transform.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

        - task_key: data_output
          depends_on:
            - task_key: data_transformation
          notebook_task:
            notebook_path: ../src/${var.project_name}/notebooks/03_output.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

      # Optional: Schedule
      schedule:
        quartz_cron_expression: "0 0 2 * * ?"  # Daily at 2am
        timezone_id: "UTC"

      # Optional: Email notifications
      email_notifications:
        on_failure:
          - ${workspace.current_user.userName}

resources/job.yml:

# Alternative: Define job in separate file
# Reference from databricks.yml with: resources: {jobs: job.yml}

resources:
  jobs:
    my_pipeline_job:
      # Same content as above

Example 2: ML Training Pipeline Bundle

databricks.yml:

bundle:
  name: ml_training_pipeline

variables:
  catalog:
    description: "Unity Catalog for ML assets"
    default: "ml_dev"

  schema:
    description: "Schema for models and features"
    default: "churn_model"

  experiment_name:
    description: "MLflow experiment path"

targets:
  dev:
    mode: development
    variables:
      catalog: "ml_dev"
      experiment_name: "/Users/${workspace.current_user.userName}/experiments/churn_dev"

  prod:
    mode: production
    variables:
      catalog: "ml_prod"
      experiment_name: "/Shared/experiments/churn_prod"

resources:
  jobs:
    ml_training_job:
      name: ml_training_${bundle.target}

      tasks:
        - task_key: data_preparation
          notebook_task:
            notebook_path: ../src/ml_training/notebooks/01_data_prep.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

        - task_key: feature_engineering
          depends_on:
            - task_key: data_preparation
          notebook_task:
            notebook_path: ../src/ml_training/notebooks/02_features.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

        - task_key: model_training
          depends_on:
            - task_key: feature_engineering
          notebook_task:
            notebook_path: ../src/ml_training/notebooks/03_training.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}
              experiment_name: ${var.experiment_name}

        - task_key: model_registration
          depends_on:
            - task_key: model_training
          notebook_task:
            notebook_path: ../src/ml_training/notebooks/04_register.py
            base_parameters:
              catalog: ${var.catalog}
              schema: ${var.schema}

Example 3: Medallion Architecture Bundle

databricks.yml:

bundle:
  name: medallion_pipeline

variables:
  catalog:
    description: "Unity Catalog name"
    default: "de_dev"

targets:
  dev:
    mode: development
    variables:
      catalog: "de_dev"

  prod:
    mode: production
    variables:
      catalog: "de_prod"

resources:
  jobs:
    medallion_job:
      name: medallion_pipeline_${bundle.target}

      tasks:
        # Bronze layer - raw ingestion
        - task_key: bronze_ingestion
          notebook_task:
            notebook_path: ../src/medallion/notebooks/bronze_ingest.py
            base_parameters:
              catalog: ${var.catalog}
              bronze_schema: "bronze"

        # Silver layer - cleaned/validated
        - task_key: silver_transformation
          depends_on:
            - task_key: bronze_ingestion
          notebook_task:
            notebook_path: ../src/medallion/notebooks/silver_transform.py
            base_parameters:
              catalog: ${var.catalog}
              bronze_schema: "bronze"
              silver_schema: "silver"

        # Gold layer - business aggregates
        - task_key: gold_aggregation
          depends_on:
            - task_key: silver_transformation
          notebook_task:
            notebook_path: ../src/medallion/notebooks/gold_aggregate.py
            base_parameters:
              catalog: ${var.catalog}
              silver_schema: "silver"
              gold_schema: "gold"

      schedule:
        quartz_cron_expression: "0 0 * * * ?"  # Hourly
        timezone_id: "UTC"

Notebook Parameter Example

Parameterized notebook (01_data_prep.py):

# Databricks notebook source
# MAGIC %md
# MAGIC # Data Preparation
# MAGIC
# MAGIC Loads and prepares data for transformation

# COMMAND ----------
# Widget parameterization with defaults
try:
    catalog = dbutils.widgets.get("catalog")
except:
    catalog = "dev_catalog"

try:
    schema = dbutils.widgets.get("schema")
except:
    schema = "pipeline_data"

try:
    batch_date = dbutils.widgets.get("batch_date")
except:
    from datetime import date
    batch_date = str(date.today())

print(f"Running with parameters:")
print(f"  Catalog: {catalog}")
print(f"  Schema: {schema}")
print(f"  Batch Date: {batch_date}")

# COMMAND ----------
from pyspark.sql import functions as F

# Load data using parameterized catalog/schema
df = spark.table(f"{catalog}.{schema}.source_data")

# Filter by batch date
df_filtered = df.filter(F.col("date") == batch_date)

print(f"Loaded {df_filtered.count()} records for {batch_date}")

# COMMAND ----------
# Save prepared data
output_table = f"{catalog}.{schema}.prepared_data"

df_filtered.write \
    .format("delta") \
    .mode("overwrite") \
    .option("overwriteSchema", "true") \
    .saveAsTable(output_table)

print(f"Saved to {output_table}")

Deployment Commands

Validate Bundle

# Validate bundle structure and configuration
databricks bundle validate -t dev

# Common validation errors:
# - Invalid YAML syntax
# - Missing required fields
# - Invalid notebook paths
# - Undefined variables

Deploy Bundle

# Deploy to development environment
databricks bundle deploy -t dev

# Deploy to production environment
databricks bundle deploy -t prod

# What happens:
# - Bundle uploaded to workspace
# - Jobs created/updated
# - Notebooks synced
# - Resources configured

Run Deployed Job

# IMPORTANT: Ask user first!
# "Do you want to run the job now?"

# If confirmed:
databricks bundle run my_job -t dev

# Monitor output for:
# - Run URL (for tracking)
# - Run status
# - Error messages

Error Handling

Validation Errors

Error: Invalid notebook path: src/notebooks/01_prep.py

Cause: Path doesn't account for relative resolution

Fix: Use ../src/notebooks/01_prep.py (relative to resources/)

---

Error: Variable 'catalog' is not defined

Cause: Used ${var.catalog} without defining in variables section

Fix: Add to databricks.yml:

variables:
  catalog:
    description: "Unity Catalog name"

---

Error: YAML syntax error at line 15

Cause: Invalid YAML (indentation, missing quotes, etc.)

Fix: Check YAML syntax, ensure consistent indentation (2 spaces)

Deployment Errors

Error: Permission denied: cannot create job

Cause: Insufficient workspace permissions

Fix: Check user has job creation permissions in workspace

---

Error: Notebook not found: /Workspace/...

Cause: Notebook doesn't exist at specified path

Fix: Verify notebook was created in src/ directory, check path in job definition

Integration with Other Skills

Receives From

• databricks-testing - Tested, working code
• databricks-unity-catalog - Schema and table names to use

Used By

• databricks-ml-pipeline - Packages ML training pipelines
• databricks-data-engineering - Packages data pipelines

Best Practices

1. Always Parameterize

• Never hard-code catalog/schema names
• Use variables for environment-specific values
• Use widgets in notebooks with try/except defaults

2. Use Serverless Compute

• Don't define new_cluster
• Rely on Databricks serverless
• Faster startup, better cost optimization

3. Validate Before Deploy

• Always run databricks bundle validate first
• Fix all validation errors
• Then deploy

4. Use Meaningful Names

• Job names: project_name_job_${bundle.target}
• Task keys: Descriptive (data_prep, model_training)
• Clear variable names

5. Document with Comments

• Add descriptions to all variables
• Comment complex job configurations
• Include README in project

6. Multi-Environment from Day 1

• Define dev, staging, prod targets upfront
• Use same bundle for all environments
• Only variables differ per environment

Security Reminders

• Never embed tokens or secrets in databricks.yml
• Use environment variables for credentials
• Set proper job permissions
• Use service principals for production

Summary

This skill packages and deploys Databricks Asset Bundles:

• Create: Generate project structure, databricks.yml, job definitions
• Parameterize: Variables for catalogs, schemas, environments
• Validate: Automatic validation (no confirmation)
• Deploy: Automatic deployment (no confirmation)
• Run: Manual job execution (requires user confirmation)
• Multi-environment: Support dev/staging/prod with same bundle

Use this skill after testing code with databricks-testing to deploy production-ready pipelines.