March 2018

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

Note

We are now providing Databricks Runtime deprecation notices in Databricks Runtime Release Notes.

Command execution details

March 27 - April 3, 2018: Version 2.68

When you run a command in a notebook, you will now see detailed progress information.

Databricks CLI 0.6.1 supports --profile

March 27 - April 3, 2018: Version 2.68

Databricks CLI 0.6.1 supports --profile in all positions.

See Databricks CLI.

ACLs enabled by default for new Operational Security customers

March 27 - April 3, 2018: Version 2.68

Access control lists (ACLs) are now enabled by default for all new customers with the Databricks Operational Security Package. Existing customers must continue to enable ACLs manually.

See Manage Access Control.

New doc site theme

March 21, 2018

We’ve updated the look and feel of our documentation site. We hope you like it!

Cluster event log

Mar 13-20, 2018: Version 2.67

The cluster details page has a new Event Log tab that displays important cluster life cycle events. Historical events can be viewed for 60 days, which is comparable with other data retention times in Databricks.

See Cluster Event Log for more information.

Databricks CLI: 0.6.0 release

Mar 13, 2018: databricks-cli 0.6.0

Databricks CLI now supports Python 3.

See Databricks CLI for more information.

Job run management

Mar 13-20, 2018: Version 2.67

You can now delete a job run in the job details page and the job run page.

The job run Get Output endpoint is GA, and the maximum output returned has been increased to 5 MB.

Edit cluster permissions now requires edit mode

Mar 13-20, 2018: Version 2.67

Previously it was possible to edit a cluster’s permissions without clicking Edit, which was inconsistent with other cluster attributes.

A side effect of this change is that you can no longer edit cluster permissions while a cluster is pending.

Databricks ML Model Export

March 1, 2018

The documentation now covers how to use Databricks ML Model Export, which allows you to export models and full ML pipelines from Apache Spark. These exported models and pipelines can be imported into other (Spark and non-Spark) platforms to do scoring and make predictions. Model Export is targeted at low-latency, lightweight ML-powered applications.

Note

This feature requires Databricks Runtime 4.0+.

See Exporting and Importing ML Models for more information.