A 400 Bad Request error in Databricks typically indicates that the server cannot process your request due to an issue on the client side. This could be caused by a variety of factors, including:

Common Causes and Solutions:

1. Incorrect Syntax or Invalid Parameters:
   • Check your API request: Double-check the syntax of your API calls, ensuring that endpoints, parameters, and request bodies are correctly formatted.
   • Validate parameter values: Verify that all parameter values are within the allowed ranges and adhere to the expected data types.
2. Authentication Issues:
   • API token: Ensure that your API token is valid and has not expired.
   • Permissions: Verify that the user or service account associated with the token has the necessary permissions to access the requested resources.
3. Incorrect URL or Endpoint:
   • Workspace URL: Confirm that you are using the correct Databricks workspace URL.
   • API version: Make sure you are targeting the correct API version in your requests.
4. Data Format Issues:
   • JSON formatting: If you send JSON data, ensure it is well-formed and adequately escaped.
   • Content-Type header: Verify that you are setting the Content-Type header to application/JSON when sending JSON payloads.

Specific Scenarios:

• SQL Execution API: If you are using the SQL Execution API, review the query syntax and ensure that all table and column names are correct.
• Machine Learning Models: When working with machine learning models, ensure your input data matches the expected schema and format.

Additional Tips:

• Enable Debugging: If available, enable debug logging to get more detailed error messages, which can help pinpoint the problem.
• Check Databricks Status: Occasionally, Databricks might experience service disruptions. Check the Databricks status page to see if any known issues are reported.
• Contact Databricks Support: If you have exhausted all other options, contact Databricks support for assistance.