kedro-databricks 0.1.8

A plugin to run Kedro pipelines on Databricks.

kedro-databricks

Kedro plugin to develop Kedro pipelines for Databricks. This plugin strives to provide the ultimate developer experience when using Kedro on Databricks. The plugin provides three main features:

1. Initialization: Transform your local Kedro project into a Databricks Asset Bundle project with a single command.
2. Generation: Generate Asset Bundle resources definition with a single command.
3. Deployment: Deploy your Kedro project to Databricks with a single command.

Overview

The plugin provides a new kedro-databricks CLI command group with the following commands:

• kedro databricks init: Initialize a Kedro project for Databricks.
• kedro databricks bundle: Generate Asset Bundle resources definition.
• kedro databricks deploy: Deploy a Kedro project to Databricks.

Prerequisites

• Databricks CLI must be installed and configured. See Databricks CLI for more information.
  • Click here for help on installation
  • Click here for help on configuration

Installation

pip install kedro-databricks

Usage

Initialization

To initialize a Kedro project for Databricks, run:

kedro databricks init

This command will create the following files:

├── databricks.yml # Databricks Asset Bundle configuration
├── conf/
│   └── base/
│       └── databricks.yml # Workflow overrides

The databricks.yml file is the main configuration file for the Databricks Asset Bundle. The conf/base/databricks.yml file is used to override the Kedro workflow configuration for Databricks.

Override the Kedro workflow configuration for Databricks in the conf/base/databricks.yml file:

# conf/base/databricks.yml

default: # will be applied to all workflows
    job_clusters:
        - job_cluster_key: default
          new_cluster:
            spark_version: 7.3.x-scala2.12
            node_type_id: Standard_DS3_v2
            num_workers: 2
            spark_env_vars:
                KEDRO_LOGGING_CONFIG: /dbfs/FileStore/<package-name>/conf/logging.yml
    tasks: # will be applied to all tasks in each workflow
        - task_key: default
          job_cluster_key: default

<workflow-name>: # will only be applied to the workflow with the specified name
    job_clusters:
        - job_cluster_key: high-concurrency
          new_cluster:
            spark_version: 7.3.x-scala2.12
            node_type_id: Standard_DS3_v2
            num_workers: 2
            spark_env_vars:
                KEDRO_LOGGING_CONFIG: /dbfs/FileStore/<package-name>/conf/logging.yml
    tasks:
        - task_key: default # will be applied to all tasks in the specified workflow
          job_cluster_key: high-concurrency
        - task_key: <my-task> # will only be applied to the specified task in the specified workflow
          job_cluster_key: high-concurrency

The plugin loads all configuration named according to conf/databricks* or conf/databricks/*.

Generation

To generate Asset Bundle resources definition, run:

kedro databricks bundle

This command will generate the following files:

├── resources/
│   ├── <project>.yml # Asset Bundle resources definition corresponds to `kedro run`
│   └── <project-pipeline>.yml # Asset Bundle resources definition for each pipeline corresponds to `kedro run --pipeline <pipeline-name>`

The generated resources definition files are used to define the resources required to run the Kedro pipeline on Databricks.

Deployment

To deploy a Kedro project to Databricks, run:

kedro databricks deploy

This command will deploy the Kedro project to Databricks. The deployment process includes the following steps:

1. Package the Kedro project for a specfic environment
2. Generate Asset Bundle resources definition for that environment
3. Upload environment-specific /conf files to Databricks
4. Upload /data/raw/* and ensure other /data directories are created
5. Deploy Asset Bundle to Databricks