Databricks

Manage Databricks workspaces, clusters, jobs, and notebooks. Data engineering and ML platform operations from the CLI.

33 commands

Browse by Topic

Getting Started

Quick setup and installation

3commands

Clusters

Cluster management

6commands

Jobs

Job orchestration

7commands

Workspace

Workspace management

6commands

Unity Catalog

Data governance

5commands

MLflow

ML experiment tracking

6commands

Install Databricks CLI

$ pip install databricks-cli

Install the Databricks command-line interface via pip

Configure Databricks CLI

$ databricks configure --token

Set up Databricks CLI authentication with a personal access token

Check Databricks CLI version

$ databricks --version

Display the installed Databricks CLI version

List all clusters

$ databricks clusters list

List all clusters in the workspace.

Get cluster info

$ databricks clusters get --cluster-id <cluster-id>

Get detailed information about a specific cluster.

Start cluster

$ databricks clusters start --cluster-id <cluster-id>

Start a terminated cluster.

Terminate cluster

$ databricks clusters delete --cluster-id <cluster-id>

Terminate a running cluster.

Create cluster from JSON

$ databricks clusters create --json-file cluster-config.json

Create a new cluster from a JSON configuration file.

Restart cluster

$ databricks clusters restart --cluster-id <cluster-id>

Restart a running cluster.

List all jobs

$ databricks jobs list

List all jobs in the workspace.

Get job details

$ databricks jobs get --job-id <job-id>

Get detailed information about a specific job.

Run job now

$ databricks jobs run-now --job-id <job-id>

Trigger an immediate run of a job.

Create job from JSON

$ databricks jobs create --json-file job-config.json

Create a new job from a JSON configuration.

Delete job

$ databricks jobs delete --job-id <job-id>

Delete a job from the workspace.

List job runs

$ databricks runs list --job-id <job-id>

List all runs for a specific job.

Cancel run

$ databricks runs cancel --run-id <run-id>

Cancel a running job execution.

List workspace contents

$ databricks workspace list /path/to/folder

List contents of a workspace folder.

Export notebook

$ databricks workspace export /path/to/notebook local-file.py

Export a notebook to a local file.

Import notebook

$ databricks workspace import local-file.py /path/to/notebook

Import a local file as a notebook.

Export directory

$ databricks workspace export-dir /remote/path ./local/path

Export entire workspace directory recursively.

Create directory

$ databricks workspace mkdirs /path/to/new/folder

Create a new folder in the workspace.

Delete item

$ databricks workspace rm /path/to/item --recursive

Delete a workspace item (add --recursive for folders).

List catalogs

$ databricks unity-catalog catalogs list

List all catalogs in the metastore.

List schemas

$ databricks unity-catalog schemas list --catalog-name <catalog>

List schemas in a catalog.

List tables

$ databricks unity-catalog tables list --catalog-name <cat> --schema-name <schema>

List tables in a schema.

Get table info

$ databricks unity-catalog tables get --full-name <catalog.schema.table>

Get detailed information about a table.

Grant permissions

$ databricks unity-catalog permissions update --securable-type TABLE --securable-full-name <name> --json-file grants.json

Grant permissions on a securable object.

List experiments

$ databricks experiments list

List all MLflow experiments.

Create experiment

$ databricks experiments create --name /Users/user@email.com/my-experiment

Create a new MLflow experiment.

Search runs

$ databricks runs search --experiment-ids <exp-id>

Search for runs in an experiment.

List registered models

$ databricks registered-models list

List all registered models in the registry.

Get model details

$ databricks registered-models get --name <model-name>

Get details of a registered model.

Transition model stage

$ databricks model-versions transition-stage --name <model> --version 1 --stage Production

Transition a model version to a new stage.

Discussion

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Databricks

Manage Databricks workspaces, clusters, jobs, and notebooks. Data engineering and ML platform operations from the CLI.

33 commands

Browse by Topic

Getting Started

Quick setup and installation

3commands

Clusters

Cluster management

6commands

Jobs

Job orchestration

7commands

Workspace

Workspace management

6commands

Unity Catalog

Data governance

5commands

MLflow

ML experiment tracking

6commands

Install Databricks CLI

$ pip install databricks-cli

Install the Databricks command-line interface via pip

Configure Databricks CLI

$ databricks configure --token

Set up Databricks CLI authentication with a personal access token

Check Databricks CLI version

$ databricks --version

Display the installed Databricks CLI version

List all clusters

$ databricks clusters list

List all clusters in the workspace.

Get cluster info

$ databricks clusters get --cluster-id <cluster-id>

Get detailed information about a specific cluster.

Start cluster

$ databricks clusters start --cluster-id <cluster-id>

Start a terminated cluster.

Terminate cluster

$ databricks clusters delete --cluster-id <cluster-id>

Terminate a running cluster.

Create cluster from JSON

$ databricks clusters create --json-file cluster-config.json

Create a new cluster from a JSON configuration file.

Restart cluster

$ databricks clusters restart --cluster-id <cluster-id>

Restart a running cluster.

List all jobs

$ databricks jobs list

List all jobs in the workspace.

Get job details

$ databricks jobs get --job-id <job-id>

Get detailed information about a specific job.

Run job now

$ databricks jobs run-now --job-id <job-id>

Trigger an immediate run of a job.

Create job from JSON

$ databricks jobs create --json-file job-config.json

Create a new job from a JSON configuration.

Delete job

$ databricks jobs delete --job-id <job-id>

Delete a job from the workspace.

List job runs

$ databricks runs list --job-id <job-id>

List all runs for a specific job.

Cancel run

$ databricks runs cancel --run-id <run-id>

Cancel a running job execution.

List workspace contents

$ databricks workspace list /path/to/folder

List contents of a workspace folder.

Export notebook

$ databricks workspace export /path/to/notebook local-file.py

Export a notebook to a local file.

Import notebook

$ databricks workspace import local-file.py /path/to/notebook

Import a local file as a notebook.

Export directory

$ databricks workspace export-dir /remote/path ./local/path

Export entire workspace directory recursively.

Create directory

$ databricks workspace mkdirs /path/to/new/folder

Create a new folder in the workspace.

Delete item

$ databricks workspace rm /path/to/item --recursive

Delete a workspace item (add --recursive for folders).

List catalogs

$ databricks unity-catalog catalogs list

List all catalogs in the metastore.

List schemas

$ databricks unity-catalog schemas list --catalog-name <catalog>

List schemas in a catalog.

List tables

$ databricks unity-catalog tables list --catalog-name <cat> --schema-name <schema>

List tables in a schema.

Get table info

$ databricks unity-catalog tables get --full-name <catalog.schema.table>

Get detailed information about a table.

Grant permissions

$ databricks unity-catalog permissions update --securable-type TABLE --securable-full-name <name> --json-file grants.json

Grant permissions on a securable object.

List experiments

$ databricks experiments list

List all MLflow experiments.

Create experiment

$ databricks experiments create --name /Users/user@email.com/my-experiment

Create a new MLflow experiment.

Search runs

$ databricks runs search --experiment-ids <exp-id>

Search for runs in an experiment.

List registered models

$ databricks registered-models list

List all registered models in the registry.

Get model details

$ databricks registered-models get --name <model-name>

Get details of a registered model.

Transition model stage

$ databricks model-versions transition-stage --name <model> --version 1 --stage Production

Transition a model version to a new stage.

Discussion

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