databricks-python-sdk

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

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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

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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.

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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

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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

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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

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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")

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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=run.run_id)

SQL Statement Execution

Doc: https://databricks-sdk-py.readthedocs.io/en/latest/workspace/sql/statement_execution.html

# Execute SQL query
response = w.statement_execution.execute_statement(
    warehouse_id="abc123",
    statement="SELECT * FROM catalog.schema.table LIMIT 10",
    wait_timeout="30s"
)

# Check status and get results
if response.status.state == StatementState.SUCCEEDED:
    for row in response.result.data_array:
        print(row)

# For large results, fetch chunks
chunk = w.statement_execution.get_statement_result_chunk_n(
    statement_id=response.statement_id,
    chunk_index=0
)

SQL Warehouses

Doc: https://databricks-sdk-py.readthedocs.io/en/latest/workspace/sql/warehouses.html

# List warehouses
for wh in w.warehouses.list():
    print(f"{wh.name}: {wh.state}")

# Get warehouse
warehouse = w.warehouses.get(id="abc123")

# Create warehouse
created = w.warehouses.create_and_wait(
    name="my-warehouse",
    cluster_size="Small",
    max_num_clusters=1,
    auto_stop_mins=15
)

# Start/stop
w.warehouses.start(id="abc123").result()
w.warehouses.stop(id="abc123").result()

Unity Catalog - Tables

Doc: https://databricks-sdk-py.readthedocs.io/en/latest/workspace/catalog/tables.html

# List tables in a schema
for table in w.tables.list(catalog_name="main", schema_name="default"):
    print(f"{table.full_name}: {table.table_type}")

# Get table info
table = w.tables.get(full_name="main.default.my_table")
print(f"Columns: {[c.name for c in table.columns]}")

# Check if table exists
exists = w.tables.exists(full_name="main.default.my_table")

Unity Catalog - Catalogs & Schemas

Doc (Catalogs): https://databricks-sdk-py.readthedocs.io/en/latest/workspace/catalog/catalogs.html Doc (Schemas): https://databricks-sdk-py.readthedocs.io/en/latest/workspace/catalog/schemas.html

# List catalogs
for catalog in w.catalogs.list():
    print(catalog.name)

# Create catalog
w.catalogs.create(name="my_catalog", comment="Description")

# List schemas
for schema in w.schemas.list(catalog_name="main"):
    print(schema.name)

# Create schema
w.schemas.create(name="my_schema", catalog_name="main")

Volumes

Doc: https://databricks-sdk-py.readthedocs.io/en/latest/workspace/catalog/volumes.html

from databricks.sdk.service.catalog import VolumeType

# List volumes
for vol in w.volumes.list(catalog_name="main", schema_name="default"):
    print(f"{vol.full_name}: {vol.volume_type}")

# Create managed volume
w.volumes.create(
    catalog_name="main",
    schema_name="default",
    name="my_volume",
    volume_type=VolumeType.MANAGED
)

# Read volume info
vol = w.volumes.read(name="main.default.my_volume")

Files API

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

# Upload file to volume
w.files.upload(
    file_path="/Volumes/main/default/my_volume/data.csv",
    contents=open("local_file.csv", "rb")
)

# Download file
with w.files.download(file_path="/Volumes/main/default/my_volume/data.csv") as f:
    content = f.read()

# List directory contents
for entry in w.files.list_directory_contents("/Volumes/main/default/my_volume/"):
    print(f"{entry.name}: {entry.is_directory}")

# Upload/download with progress (parallel)
w.files.upload_from(
    file_path="/Volumes/main/default/my_volume/large.parquet",
    source_path="/local/path/large.parquet",
    use_parallel=True
)

w.files.download_to(
    file_path="/Volumes/main/default/my_volume/large.parquet",
    destination="/local/output/",
    use_parallel=True
)

Serving Endpoints (Model Serving)

Doc: https://databricks-sdk-py.readthedocs.io/en/latest/workspace/serving/serving_endpoints.html

# List endpoints
for ep in w.serving_endpoints.list():
    print(f"{ep.name}: {ep.state}")

# Get endpoint
endpoint = w.serving_endpoints.get(name="my-endpoint")

# Query endpoint
response = w.serving_endpoints.query(
    name="my-endpoint",
    inputs={"prompt": "Hello, world!"}
)

# For chat/completions endpoints
response = w.serving_endpoints.query(
    name="my-chat-endpoint",
    messages=[{"role": "user", "con

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