Inside an asset bundle

Introduction to Databricks Lakehouse

Gang Wang

Senior Data Scientist

Bundle project structure

my_project/
|-- databricks.yml
|-- src/
|   |-- etl_pipeline.py
|   |-- data_quality.py
|-- resources/
|   |-- jobs/
|   |   |-- nightly_etl.yml
|   |-- pipelines/
|       |-- sales_pipeline.yml
|-- tests/
    |-- test_etl.py

databricks.yml - the central config file
src/ - your notebooks and code
resources/ - job and pipeline definitions
tests/ - optional test files

The databricks.yml file

bundle:
  name: sales_analytics

workspace:
  host: https://myworkspace.databricks.com

targets:
  dev:
    default: true
    workspace:
      root_path: /Users/me/dev
  production:
    workspace:
      root_path: /Shared/production
    permissions:
      - level: CAN_MANAGE
        group_name: data_engineers

Defining resources

layers: databricks.yml (top), Resources - Jobs - Pipelines - Dashboards, Code - Notebooks - Python scripts, Targets - dev - staging - production

resources:
  jobs:
    nightly_etl:
      name: "Nightly ETL Pipeline"
      schedule:
        quartz_cron: "0 0 2 * * ?"
      tasks:
        - task_key: ingest
          notebook_task:
            notebook_path: src/etl.py

Bundle CLI commands

Command	Purpose
`bundle validate`	Check config for errors
`bundle deploy`	Deploy to a target
`bundle run`	Trigger a deployed job
`bundle destroy`	Remove deployed resources

# Validate before deploying
databricks bundle validate

# Deploy to production
databricks bundle deploy \
  --target production

# Trigger a run
databricks bundle run nightly_etl

Deployment workflow

flowchart: Deployment workflow

Local development - edit code and validate configs
Version control - commit changes to Git
CI/CD automation - deploy to staging and run tests
Production promotion - deploy with confidence

Summary

databricks.yml defines your project, targets, and resources
Resources include jobs, pipelines, and dashboards
Targets map to environments (dev, staging, production)
CLI commands: validate, deploy, run, destroy

Let's practice!

Introduction to Databricks Lakehouse