Infrastructure-as-Code

While Laktory offers a simplified interface for declaring and deploying Databricks resources, it is built on top of Infrastructure as Code (IaC) backends to provide a robust and proven experience.

Backends

Declarative Automation Bundles (DAB)

Declarative Automation Bundles is Databricks' native IaC solution, built directly into the Databricks CLI. Laktory integrates with DAB through a Python resource hook: declare your pipelines as individual YAML files, register the hook in databricks.yml, and run databricks bundle deploy. Laktory generates the required Job and DLT Pipeline resources automatically — no stack.yaml file required.

If your team already uses the Databricks CLI and DAB to manage workspace resources, this is the fastest path to getting Laktory pipelines deployed. See the DAB concept page for full details.

Terraform

Terraform is one of the most popular IaC tools available. It allows users to define and provision infrastructure resources using a declarative configuration language, automating the creation and management of both cloud and on-premises infrastructure. While Laktory currently supports many Terraform features, additional capabilities may be added in the future.

Terraform

Deploying resources is achieved by creating a stack in a yaml file and using laktory CLI to run the deployment.

stack.yaml

name: workspace
resources:
  providers:
    databricks:
      host: ${vars.DATABRICKS_HOST}
      token: ${vars.DATABRICKS_TOKEN}
  pipelines:
    pl-stock-prices:
      name: pl-stock-prices
      libraries:
        - notebook:
            path: /pipelines/dlt_brz_template.py

To validate the configuration and preview the deployment, run:

laktory preview

Once ready to deploy, use:

laktory deploy

If your stack defines multiple environments, you can target a specific environment with:

laktory deploy --env dev

Declarative Automation Bundles

If your team already uses the Databricks CLI, DAB integration requires only a few additions to your existing databricks.yml.

databricks.yml

variables:
  dab_workspace_root:
    default: ${workspace.root_path}
  laktory_pipelines_dir:
    default: ./laktory/pipelines

sync:
  paths:
    - ./laktory
  include:
    - ./laktory/.build/**

python:
  venv_path: .venv
  resources:
    - 'laktory.dab:build_resources'

Each pipeline YAML file in laktory_pipelines_dir is loaded automatically. Laktory generates the corresponding Databricks Job or DLT Pipeline resource and writes the pipeline config to laktory/.build/ for DAB to sync to the workspace. Then deploy as usual:

databricks bundle deploy --target dev

See the DAB concept page for full details including orchestrator options, variable injection, and settings.