Databricks API


                     Databricks API

Here’s a comprehensive guide to the Databricks API, including critical areas, use cases, and how to get started:

Understanding the Databricks REST API

  • RESTful:  The Databricks REST API adheres to the REST (Representational State Transfer) architectural style, providing a standard way to interact with the Databricks platform programmatically.
    • Operations: The API covers various operations, including Workspace management, data imports/exports, and notebook and folder management.
    • Clusters: Create, start, terminate, and manage Spark clusters.
    • Jobs: Schedule and run jobs access run results.
    • Libraries: Install and manage libraries on clusters.
    • Permissions: Control access and collaboration within workspaces.
    • And many more…

Databricks API Use Cases

    • Automation and Integration: Automate the creation and management of clusters and jobs.
    • Integrate Databricks with CI/CD pipelines for streamlined development.
    • Trigger Databricks tasks from external systems or applications.
    • Custom Tooling: Develop custom tools and dashboards to manage and monitor Databricks resources.
    • Build interfaces tailored to your specific needs.
    • Advanced Workflows: Orchestrate complex sequences of data processing and machine learning tasks.
    • Implement programmatic logic within notebooks using the API.

Getting Started

    • Authentication: To authenticate your requests, you’ll need an API token. To generate one, follow these steps: Log in to your Databricks workspace.
    • Go to User Settings.
    • Under Access Tokens, click “Generate New Token” and give it a helpful description.
    • API Documentation: Refer to the detailed reference documentation: REST API 2.0: [invalid URL removed]
    • Language/Library Choice: Direct HTTP Requests: Use libraries like requests in Python or equivalent tools in other languages to make HTTP requests to the API endpoints.
    • Databricks-api Python Library:  Offers a simplified interface (

Example (Python with the requests library)

import requests

# Replace with your Databricks workspace URL and token
DATABRICKS_TOKEN = “your_api_token”

headers = {“Authorization”: f”Bearer {DATABRICKS_TOKEN}”}

# API call to list clusters
response = requests.get(f”{DATABRICKS_HOST}/api/2.0/clusters/list”, headers=headers)

if response.status_code == 200:
clusters = response.json()[‘clusters’]
print(“Error:”, response.text)

Important Notes

  • Rate limits: Be aware of API rate limits to avoid getting throttled.
  • Permissions: The actions you can perform with the API are determined by your Databricks user permissions.

Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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