New Features
At-Source Databricks on AWS
Databricks assets can now be queried in Spaces and exported on AWS-hosted platforms, with the data staying in your own Unity Catalog throughout. Harbr catalogues the metadata; the data itself never moves.
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In Spaces, queries run live against your Databricks SQL warehouse, so results always reflect the current state of the data.
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For Export, data is read at the point you export, to produce the export file.
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You pay for Databricks compute only when you query or export. Nothing runs simply because the asset is catalogued in Harbr.
Spaces and Export are enabled separately by an Ecosystem Admin, and Export requires an additional platform-side deployment. To have this configured, please contact your Harbr Account Manager.
For operator set-up, see Configuring Databricks At-Source Assets on AWS.
New MCP Tools for Subscribing, Exporting and Data Sharing
EARLY ACCESS
Building on the Discovery MCP Tools introduced in 5.31, your AI assistant can now act on your behalf for several new workflows, all from within a normal chat conversation.
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Subscribe to a product on a self-serve plan, request access to an approval-gated plan and track its status, or activate a subscription that is on hold pending terms acceptance.
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Create a data share of a product or a standalone asset over Databricks, either an open, token-based share usable by any recipient, or a direct Databricks-to-Databricks share into the recipient's own Unity Catalog.
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Export a product, setting the destination, name, description, refresh schedule, and email notifications.
If you do not see the new tools right away, disconnect and reconnect your AI assistant to refresh the available tool list.
For user guidance, please see Using the Harbr MCP.
For operator set-up, see Configuring the MCP Server.
If you are interested in configuring this feature, please contact your Harbr Account Manager.
Improvements
Additional Connector Types Supported by Capability Checks
EARLY ACCESS
Connector capability checks now run for Google Cloud Storage, Azure, Amazon S3, DB2, SFTP, Power BI and Looker, alongside the database connectors covered since 5.31.
The checks test a connector from two places before you set it live: from Harbr, and from the processing environment where your jobs actually run. A connector that would have failed later is caught during setup instead.
This release extends that same checking to further connector types: Google Cloud Storage, Azure, Amazon S3, DB2, SFTP, Power BI, and Looker.
For user guidance, see the relevant section within Create a Connector.
For operator set-up, see Configuring Connector Capability Checks.
More Granular Control Over User Deletion
EARLY ACCESS
Ecosystem admins can now safely remove a user directly from the platform, with control over who can do so, whether that's a named list of admins or all ecosystem admins. Deletion is a soft deactivation rather than a hard delete: the person's access is revoked immediately, but everything they created is retained and re-attributed to "Name (Deactivated)", nothing they owned is transferred or lost.
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A user cannot be deleted while they have a running Space, a running export, a scheduled or in-flight task, or own a Space with active collaborators, the platform explains exactly what needs resolving first before the deletion can proceed.
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Deleting a user who was invited but never registered, or a service account, is out of scope for this release.
For operator set-up, see Delete a User.
Improvements to Connector Behaviour
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The SFTP connector is more resilient to transient network conditions.