November 2024

These features and Databricks platform improvements were released in November 2024.

Note

Releases are staged. Your Databricks account might not be updated until a week or more after the initial release date.

Expanded support for Python debugger in notebooks

November 18, 2024

The built-in Python debugger in Databricks notebooks is now supported on serverless compute and compute configured with shared access mode in Databricks Runtime 14.3 LTS or above. To learn more about the debugger, see Debug notebooks.

Improved web terminal experience

November 18, 2024

Databricks made the following improvements to the web terminal experience:

  • Support for configuration files: You can now set persistent configurations for your Databricks web terminal with .bashrc configuration files. See Configure your web terminal.

  • Command cycle history: You can now cycle through commands executed in previous sessions with the “Up” and “Down” arrow keys. This makes it easy to recall and reuse previous commands without retyping them.

  • Shared Cluster Support: Our improved web terminal is available on shared clusters in Databricks Runtime 15.1 and above.

To learn more about using the web terminal, see Run shell commands in Databricks web terminal.

Query history and query profile support for Delta Live Tables

November 15, 2024

Databricks now offers improved tools for monitoring query performance in Delta Live Tables pipelines. See Access query history for Delta Live Tables pipelines.

Reuse SQL outputs as _sqldf in subsequent SQL cells

November 15, 2024

In Databricks Runtime 14.3 and above, running a SQL query in a Databricks notebook generates an implicit DataFrame (_sqldf) that you can use in downstream SQL cells. This adds to the support for using for using the _sqldf variable in Python cells introduced in Databricks Runtime 13.3. See Explore SQL cell results.

Warehouses system table is now available (Public Preview)

November 15, 2024

The system.compute.warehouses table records SQL warehouse configurations. Each row is a snapshot of a SQL warehouse’s properties at a specific point in time. A new snapshot is created when the properties change. See Warehouses system table reference.

Cross-platform view sharing (Public Preview)

November 14, 2024

Databricks recipients can now query shared views using any Databricks compute resource. Previously, if a recipient’s Databricks account was different from the provider’s account, recipients could only query a shared view using a serverless SQL warehouse. See Read shared views.

View sharing also now extends to open sharing connectors. See Read data shared using Delta Sharing open sharing (for recipients).

Databricks Runtime 16.0 is GA

November 11, 2024

Databricks Runtime 16.0 and Databricks Runtime 16.0 ML are now generally available.

See Databricks Runtime 16.0 and Databricks Runtime 16.0 for Machine Learning.

Breaking change: Hosted RStudio is end-of-life

November 11, 2024

In Databricks Runtime 16.0 and above, Databricks-hosted RStudio Server is end-of-life and unavailable on any Databricks workspace. To learn more and see a list of alternatives to RStudio, see Hosted RStudio Server deprecation.

Updated and unified read-only code blocks

November 8, 2024

A new read-only code block is now available across the Catalog Explorer, alerts page, and Genie.

Increased Quick Fix recommendations

November 8, 2024

Databricks Assistant Quick Fix now identifies and suggests corrections for a broader range of common SQL and Python errors. With a 50% increase in error detection, Quick Fix can help you debug your code faster. See Quick Fix.

Databricks Runtime 14.2 series support ends

November 5, 2024

Support for Databricks Runtime 14.2 and Databricks Runtime 14.2 for Machine Learning ended on November 5. See Databricks support lifecycles.

Serverless compute for workflows (Public Preview)

November 4, 2024

Serverless compute for workflows is now available in Public Preview. Serverless compute for workflows allows you to run your Databricks job without configuring and deploying infrastructure. With serverless compute for workflows, Databricks efficiently manages the compute resources that run your job, including optimizing and scaling compute for your workloads. See Run your Databricks job with serverless compute for workflows.

Serverless compute for notebooks (Public Preview)

November 4, 2024

Serverless compute for notebooks is now available in Public Preview. Serverless compute for notebooks gives you on-demand access to scalable compute in notebooks, letting you immediately write and run your Python or SQL code. See Serverless compute for notebooks.