Announcements
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Create, view, and monitor Foundry peering connections using Peer Manager
Date published: 2025-11-06
Peer Manager enables you to view and monitor jobs associated with an established peering connection that synchronizes objects and links between Foundry enrollments in real-time as well as mediates changes made across ontologies. The application will be generally available across all enrollments the third week of November.
What is peering?
Peering enables organizations to establish secure, real-time Ontology data synchronization across distinct Foundry enrollments. Peer Manager is the central home for administering peering in Foundry. From Peer Manager, space administrators can create peer connections, monitor peering jobs, and configure data to peer.
What is Peer Manager?
After you create a peer connection, you can use Peer Manager's home page to garner information about your new connection and all other connections configured between your enrollment and other enrollments. Peer connections support the import and export of Foundry objects and their links as well as object sets configured in Object Explorer.
The Peer Manager home page provides an overview of all configured Peer Connections.
Select a connection to launch its Overview window, where you can track the health of each peer connection by viewing the status of individual peering jobs.
Peer Manager's Overview window offers a unified view of the status and health of peering jobs within a connection.
Select Ontology from the top ribbon to peer objects across an established connection, where Peer Manager enables you to peer all or a selection of properties on the object.
Learn more about object peering in Peer Manager.
Peer Manager's Ontology window enables you to peer object types and their links across a peer connection.
What's next on the development roadmap?
The ability to configure Artifact peering will be available in Peer Manager by the end of 2025. Contact Palantir Support with questions about peering or Peer Manager on your enrollment.
Use Pipeline Builder external pipelines to push down compute to Databricks
Date published: 2025-11-06
Pipeline Builder now offers the ability to create external pipelines using third-party compute engines, with Databricks as the first supported provider. This capability is in beta.
External pipelines require virtual table inputs and outputs from the same source as your compute. When using external pipelines, compute is orchestrated by Foundry and pushed down to the source system for execution.
Foundry’s external compute orchestration provides you with the flexibility to choose the most appropriate technology for your workload, use case, and architecture requirements. Pipelines built with external compute can also be composed together with Foundry-native compute pipelines using Foundry’s scheduling tools, allowing you to easily orchestrate complex multi-technology pipelines using the exact right compute at every step along the way.
With this improvement, you can now push down compute to Databricks using either code-based Python transforms or point-and-click Pipeline Builder boards. Learn more about creating external pipelines in Pipeline Builder.
Enabling push down compute in Pipeline Builder.
External pipeline with pushdown compute in Pipeline Builder.
Your feedback matters
As we continue to add features to Pipeline Builder, we want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community ↗ using the pipeline-builder tag ↗.
Import Iceberg and Delta virtual tables into Code Workspaces
Date published: 2025-11-06
Iceberg ↗ and Delta ↗ tables can now be imported as virtual tables into JupyterLab® code workspaces, providing more flexibility when working with externally stored data at large scales. Delta and Iceberg tables are open source table formats that enable reliable, scalable, and efficient management of large datasets, including tables stored in Databricks.
JupyterLab® code workspaces now support read and write capabilities for Iceberg and Delta tables, and provide table-specific code snippets in the Data panel to facilitate development.
A highlighted code snippet in the Data panel.
This feature enables running interactive Python notebooks against data stored and cataloged externally to Foundry in Iceberg and Delta tables, supporting a wide range of data science, analytics, and machine learning workflows.
Learn more about virtual tables and Code Workspaces.
Jupyter®, JupyterLab®, and the Jupyter® logos are trademarks or registered trademarks of NumFOCUS. All third-party trademarks (including logos and icons) referenced remain the property of their respective owners. No affiliation or endorsement is implied.
Package widget sets automatically with Marketplace products
Date published: 2025-11-04
Widget sets created in Custom Widgets can now be included as content in Marketplace products.
When you add a Workshop module that uses a widget set to a Marketplace product, the widget set is automatically packaged. Widget sets can also be manually packaged independently, allowing you to build Workshop modules on top of them.
If a widget set had Ontology API access enabled in the source environment, it will be installed with access disabled by default. After installation, you must manually enable Ontology API access on the widget set if needed.
Published Marketplace product containing a Workshop module that uses a widget set.
We want to hear from you
As we continue to develop new features for custom widgets, we want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community ↗ and use the custom-widgets ↗ tag.
Roll back datasets and queue snapshots in Data Lineage
Date published: 2025-11-04
Dataset rollback is now available in Data Lineage, giving you greater control over your data pipelines. Whether you encounter an outage, errors in your pipeline logic, or unexpected upstream data, dataset rollback provides a fast, reliable way to revert your datasets to a stable state. In addition, you can now queue snapshots, allowing datasets to snapshot automatically on their next build.
Dataset rollbacks provide several key benefits:
- Quickly revert to an earlier version of your dataset.
- Preserve incremental build workflows.
- Recover from data issues or outages.
Roll back a dataset
To get started with dataset rollback, open your dataset in Data Lineage and select a previous successful transaction in the History tab. You can roll back your dataset to that transaction by selecting Roll back to transaction.
The Roll back to transaction option, listed in a selected transaction's Overview tab.
Snapshot on next build
To queue a snapshot on your dataset's next build, open a dataset in Data Lineage and select Force snapshot In the History tab in the bottom panel.
The Force snapshot option in the History tab.
Note that you will need to acknowledge that this action cannot be undone before proceeding.
Important considerations
- Only users with the
Editorrole can perform rollbacks to ensure secure operations. - Rollbacks are only supported on transactional datasets, and you can only roll back to successful transactions.
- It is not possible to roll back to a transaction that was deleted based on a retention policy. However, you can roll back to a transaction that was deleted by a dataset rollback in Data Lineage.
- If your dataset backs an object type in object storage v2, you will need to reindex after rolling back.
Dataset rollback allows you to build, experiment, and iterate on your pipelines with confidence; the ability to revert to a stable state is available whenever you need it.
Your feedback matters
We want to hear about your experience and welcome your feedback as we develop more features in Data Lineage. Share your thoughts with Palantir Support channels or on our Developer Community ↗ using the data-lineage tag ↗.
Learn more about dataset rollback.
Flexible rebasing and conflict resolution in Ontology Manager
Date published: 2025-11-04
Ontology Manager now offers an improved rebasing and conflict resolution experience that gives you greater flexibility and control when managing branch changes. You can now rebase at any point without creating a proposal, view changes from both Main and your branch simultaneously, and resolve merge conflicts using multiple approaches—either through the Conflicts tab in the Save dialog or directly in the Ontology Manager interface for conflict resolution. This enhanced workflow prevents situations where unresolvable errors block your progress. This feature is available the week of November 3 across all enrollments.
Visit the documentation on testing changes in the ontology.
What is rebasing?
While you introduce changes on your branch, Main can also update with new changes made by others. Rebasing incorporates the latest changes from Main into your current branch to keep it up to date.
Resolve merge conflicts by choosing between changes from Main or your current branch directly in Ontology Manager.
During a rebase, Ontology Manager enters a new state where you can view and access changes from both Main and your branch. You may resolve merge conflicts by choosing between changes from Main or your current branch from the Conflicts tab in the save dialog. Alternatively, you can resolve conflicts by editing the ontology resource directly. This flexibility prevents situations where users become stuck due to unresolvable errors after conflict resolution.
Known limitations
Complex cases of schema migrations or datasource replacements are not yet handled by this rebasing experience. Refer to the known limitations section of the documentation for an alternative solution. We are actively working to resolve these limitations.
We want to hear from you
As we continue to develop new features for Foundry Branching, we want to hear about your experiences and welcome your feedback. Share your thoughts with Palantir Support channels or our Developer Community ↗ and use the foundry-branching ↗ tag.