/databricks-python-sdk

name: databricks-python-sdk
description: "Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs."

Databricks Development Guide

This skill provides guidance for Databricks SDK, Databricks Connect, CLI, and REST API.

**SDK Documentation:** https://databricks-sdk-py.readthedocs.io/en/latest/ **GitHub Repository:** https://github.com/databricks/databricks-sdk-py

---

Environment Setup

• Use existing virtual environment at `.venv` or use `uv` to create one
• For Spark operations: `uv pip install databricks-connect`
• For SDK operations: `uv pip install databricks-sdk`
• Databricks CLI version should be 0.278.0 or higher

Configuration

• Default profile name: `DEFAULT`
• Config file: `~/.databrickscfg`
• Environment variables: `DATABRICKS_HOST`, `DATABRICKS_TOKEN`

---

Databricks Connect (Spark Operations)

Use `databricks-connect` for running Spark code locally against a Databricks cluster.

from databricks.connect import DatabricksSession

# Auto-detects 'DEFAULT' profile from ~/.databrickscfg
spark = DatabricksSession.builder.getOrCreate()

# With explicit profile
spark = DatabricksSession.builder.profile("MY_PROFILE").getOrCreate()

# Use spark as normal
df = spark.sql("SELECT * FROM catalog.schema.table")
df.show()

**IMPORTANT:** Do NOT set `.master("local[*]")` - this will cause issues with Databricks Connect.

---

Direct REST API Access

For operations not yet in SDK or overly complex via SDK, use direct REST API:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

# Direct API call using authenticated client
response = w.api_client.do(
    method="GET",
    path="/api/2.0/clusters/list"
)

# POST with body
response = w.api_client.do(
    method="POST",
    path="/api/2.0/jobs/run-now",
    body={"job_id": 123}
)

**When to use:** Prefer SDK methods when available. Use `api_client.do` for:

• New API endpoints not yet in SDK
• Complex operations where SDK abstraction is problematic
• Debugging/testing raw API responses

---

Databricks CLI

# Check version (should be >= 0.278.0)
databricks --version

# Use specific profile
databricks --profile MY_PROFILE clusters list

# Common commands
databricks clusters list
databricks jobs list
databricks workspace ls /Users/me

---

SDK Documentation Architecture

The SDK documentation follows a predictable URL pattern:

Base: https://databricks-sdk-py.readthedocs.io/en/latest/

Workspace APIs:  /workspace/{category}/{service}.html
Account APIs:    /account/{category}/{service}.html
Authentication:  /authentication.html
DBUtils:         /dbutils.html

Workspace API Categories

| Category | Services | |----------|----------| | `compute` | clusters, cluster_policies, command_execution, instance_pools, libraries | | `catalog` | catalogs, schemas, tables, volumes, functions, storage_credentials, external_locations | | `jobs` | jobs | | `sql` | warehouses, statement_execution, queries, alerts, dashboards | | `serving` | serving_endpoints | | `vectorsearch` | vector_search_indexes, vector_search_endpoints | | `pipelines` | pipelines | | `workspace` | repos, secrets, workspace, git_credentials | | `files` | files, dbfs | | `ml` | experiments, model_registry |

---

Authentication

**Doc:** https://databricks-sdk-py.readthedocs.io/en/latest/authentication.html

Environment Variables

DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=dapi...  # Personal Access Token

Code Patterns

# Auto-detect credentials from environment
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()

# Explicit token auth
w = WorkspaceClient(
    host="https://your-workspace.cloud.databricks.com",
    token="dapi..."
)

# Azure Service Principal
w = WorkspaceClient(
    host="https://adb-xxx.azuredatabricks.net",
    azure_workspace_resource_id="/subscriptions/.../resourceGroups/.../providers/Microsoft.Databricks/workspaces/...",
    azure_tenant_id="tenant-id",
    azure_client_id="client-id",
    azure_client_secret="secret"
)

# Use a named profile from ~/.databrickscfg
w = WorkspaceClient(profile="MY_PROFILE")

---

Core API Reference

Clusters API

**Doc:** https://databricks-sdk-py.readthedocs.io/en/latest/workspace/compute/clusters.html

# List all clusters
for cluster in w.clusters.list():
    print(f"{cluster.cluster_name}: {cluster.state}")

# Get cluster details
cluster = w.clusters.get(cluster_id="0123-456789-abcdef")

# Create a cluster (returns Wait object)
wait = w.clusters.create(
    cluster_name="my-cluster",
    spark_version=w.clusters.select_spark_version(latest=True),
    node_type_id=w.clusters.select_node_type(local_disk=True),
    num_workers=2
)
cluster = wait.result()  # Wait for cluster to be running

# Or use create_and_wait for blocking call
cluster = w.clusters.create_and_wait(
    cluster_name="my-cluster",
    spark_version="14.3.x-scala2.12",
    node_type_id="i3.xlarge",
    num_workers=2,
    timeout=timedelta(minutes=30)
)

# Start/stop/delete
w.clusters.start(cluster_id="...").result()
w.clusters.stop(cluster_id="...")
w.clusters.delete(cluster_id="...")

Jobs API

**Doc:** https://databricks-sdk-py.readthedocs.io/en/latest/workspace/jobs/jobs.html

from databricks.sdk.service.jobs import Task, NotebookTask

# List jobs
for job in w.jobs.list():
    print(f"{job.job_id}: {job.settings.name}")

# Create a job
created = w.jobs.create(
    name="my-job",
    tasks=[
        Task(
            task_key="main",
            notebook_task=NotebookTask(notebook_path="/Users/me/notebook"),
            existing_cluster_id="0123-456789-abcdef"
        )
    ]
)

# Run a job now
run = w.jobs.run_now_and_wait(job_id=created.job_id)
print(f"Run completed: {run.state.result_state}")

# Get run output
output = w.jobs.get_run_output(run_id=