Databricks Getting Started Guide: Account Setup & First Request (2026)

Getting started overview

To begin working with Databricks, users typically follow a structured path that involves setting up an account, configuring authentication, and executing an initial command. This process establishes the foundational access required for data engineering, machine learning, and analytics tasks within the Databricks Lakehouse Platform. The primary method for programmatic interaction is through the Databricks REST API, which supports operations across various components like notebooks, clusters, jobs, and Delta Live Tables (Databricks API reference). While Databricks offers SDKs for Python, Java, Scala, and R, understanding the underlying API interaction is key to robust integration.

This guide focuses on the essential steps to get a basic API call working, using Python as the example language due to its extensive use in data science and machine learning workflows on the platform. The objective is to ensure you can authenticate and successfully interact with your Databricks workspace, paving the way for more complex operations.

Quick reference table

Create an account and get keys

To start, you need a Databricks account. Databricks offers a Community Edition, which provides a free personal workspace to learn and experiment with the platform, though with limited computational resources and data storage. For more substantial projects or production use, a trial or paid account on AWS, Azure, or Google Cloud is required, allowing you to provision clusters and manage data within your cloud provider's infrastructure (Databricks pricing overview).

Account signup

1. Navigate to the Databricks trial signup page or the Community Edition signup.
2. Provide the required information, such as your name, email, and company.
3. Select your preferred cloud provider (AWS, Azure, or Google Cloud) if signing up for a trial. For Community Edition, this step is usually pre-configured.
4. Follow the on-screen instructions to complete the setup of your workspace. This typically involves connecting to your cloud account or waiting for your Community Edition workspace to be provisioned.

Generate a Personal Access Token (PAT)

Databricks uses Personal Access Tokens (PATs) for authenticating users to the REST API. PATs are long-lived tokens that grant access to your Databricks workspace and its resources. They are crucial for programmatic access and should be treated with the same security considerations as passwords.

1. Log in to your Databricks workspace.
2. Click on your username in the top-right corner of the workspace UI.
3. Select User Settings from the dropdown menu.
4. Navigate to the Access Tokens tab.
5. Click the Generate new token button.
6. Optionally, enter a comment to help identify the token's purpose (e.g., "API access for Python script").
7. Set a lifetime for the token (e.g., 90 days, 1 year, or no expiration). For initial testing, a shorter lifetime is advisable.
8. Click Generate.
9. Crucially, copy the generated token immediately. Databricks will not display the token again after this step. If you lose it, you will need to generate a new one.

Store your PAT securely. Avoid hardcoding it directly into scripts. Environment variables or secure configuration files are preferred methods for managing API keys (OAuth 2.0 Bearer Tokens specification).

Your first request

With your workspace set up and a Personal Access Token generated, you can now make your first API call. This example uses Python and the requests library to interact with the Databricks REST API. We will attempt to list the available clusters in your workspace, a common initial check.

Prerequisites for Python

Ensure you have Python installed and the requests library. If not, install it using pip:

pip install requests

API endpoint and authentication

The Databricks REST API base URL follows the format https://<your-workspace-url>/api/2.0/. Your workspace URL is the domain you use to access your Databricks workspace (e.g., adb-xxxxxxxxxxxx.xx.azuredatabricks.net or dbc-xxxxxxxx-xxxx.cloud.databricks.com).

Authentication for the Databricks REST API typically uses the Personal Access Token in the Authorization header as a Bearer token (Databricks API authentication).

Example Python script: List clusters

Create a Python file (e.g., list_clusters.py) and add the following code:

import os
import requests
import json

# Replace with your Databricks workspace URL (e.g., 'https://adb-xxxxxxxxxxxx.xx.azuredatabricks.net')
DATABRICKS_HOST = os.environ.get("DATABRICKS_HOST", "https://YOUR_DATABRICKS_WORKSPACE_URL")
# Replace with your Personal Access Token
DATABRICKS_TOKEN = os.environ.get("DATABRICKS_TOKEN", "dapiYOUR_PERSONAL_ACCESS_TOKEN")

# API endpoint to list all clusters
API_ENDPOINT = f"{DATABRICKS_HOST}/api/2.0/clusters/list"

headers = {
    "Authorization": f"Bearer {DATABRICKS_TOKEN}",
    "Content-Type": "application/json"
}

try:
    response = requests.get(API_ENDPOINT, headers=headers)
    response.raise_for_status()  # Raise an exception for HTTP errors (4xx or 5xx)

    clusters_data = response.json()
    print("Successfully connected to Databricks and retrieved clusters.")
    print(json.dumps(clusters_data, indent=2))

    if "clusters" in clusters_data and len(clusters_data["clusters"]) > 0:
        print("\nFound the following clusters:")
        for cluster in clusters_data["clusters"]:
            print(f"  - Name: {cluster.get('cluster_name')}, State: {cluster.get('state')}")
    else:
        print("\nNo clusters found in the workspace.")

except requests.exceptions.HTTPError as http_err:
    print(f"HTTP error occurred: {http_err}")  # e.g., 401 Unauthorized, 403 Forbidden
    print(f"Response body: {response.text}")
except requests.exceptions.ConnectionError as conn_err:
    print(f"Connection error occurred: {conn_err}")  # e.g., DNS failure, refused connection
except requests.exceptions.Timeout as timeout_err:
    print(f"Timeout error occurred: {timeout_err}")
except requests.exceptions.RequestException as req_err:
    print(f"An unexpected error occurred: {req_err}")
except json.JSONDecodeError:
    print(f"Failed to decode JSON from response: {response.text}")

Running the script

Before running, replace YOUR_DATABRICKS_WORKSPACE_URL and YOUR_PERSONAL_ACCESS_TOKEN with your actual values. For better security, consider setting these as environment variables:

export DATABRICKS_HOST="https://YOUR_DATABRICKS_WORKSPACE_URL"
export DATABRICKS_TOKEN="dapiYOUR_PERSONAL_ACCESS_TOKEN"
python list_clusters.py

A successful execution will print a JSON response containing details of any clusters in your Databricks workspace, or an empty list if none are running. This confirms that your API token and workspace URL are correctly configured and that you can authenticate against the Databricks API.

Common next steps

Once you have successfully made your first API call, consider these next steps to deepen your understanding and capabilities with Databricks:

• Explore the Databricks UI: Familiarize yourself with the web interface to understand how notebooks, clusters, jobs, and data are managed visually. This complements programmatic interaction and aids in debugging.
• Create a cluster: Programmatically create a Databricks cluster using the Clusters API (Databricks Clusters API documentation). This is fundamental for running any computational workload.
• Run a notebook: Upload a sample notebook and then execute it via the Jobs API (Databricks Jobs API reference). This demonstrates how to automate code execution.
• Ingest data: Experiment with loading data into Delta Lake tables, the open-source storage layer that brings reliability to data lakes (Delta Lake documentation).
• Use the Databricks SDK: While the requests library offers direct API interaction, the official Databricks SDKs (e.g., databricks-sdk for Python) provide a more idiomatic and convenient way to interact with the platform, abstracting away HTTP calls and JSON parsing. The SDK often includes built-in retry logic and error handling.
• Set up CI/CD: Integrate Databricks with your continuous integration and continuous deployment pipelines using tools like GitHub Actions or Azure DevOps to automate code deployment and job orchestration.
• Explore MLflow: For machine learning projects, integrate with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle (MLflow official documentation).

Troubleshooting the first call

Encountering issues during your first API call is common. Here are some troubleshooting tips:

• Incorrect Workspace URL: Double-check that DATABRICKS_HOST matches the exact URL of your Databricks workspace, including the https:// prefix. A common mistake is using the wrong region-specific subdomain.
• Expired or Invalid Token: Ensure your Personal Access Token is active and correctly copied. If you suspect it's invalid, generate a new one from your Databricks User Settings and update your script or environment variable. Tokens can expire if a lifetime was set during generation.
• Insufficient Permissions: The PAT might not have the necessary permissions to list clusters. By default, tokens generated by a user inherit that user's permissions. Verify that your user account has at least "Can View" permissions on clusters or is an administrator. Check the Databricks workspace admin console for user and group permissions.
• Network Connectivity Issues: Confirm that your local machine can reach the Databricks host. Firewall rules or VPN configurations might block outgoing requests. Try pinging the host or using curl from your terminal to rule out Python-specific issues.
• API Endpoint Errors: Ensure the API endpoint (e.g., /api/2.0/clusters/list) is correct and matches the Databricks API documentation for the specific operation you are trying to perform. Minor typos can lead to 404 Not Found errors.
• JSON Parsing Errors: If the API returns an error message that isn't valid JSON, response.json() will fail. Inspect response.text to see the raw error message from the server, which can provide more context.
• Rate Limiting: While less common for a first call, repeated failed attempts or rapid successive calls can trigger rate limiting. The Databricks API typically returns a 429 Too Many Requests status in such cases.
• Environment Variable Issues: If you are using environment variables, ensure they are correctly set and accessible by your Python script. Print them within the script to verify their values before making the API call.