November 2025 Release Notes

Our data pipeline monitoring is changing to a one-year maximum data retention policy. Any stored data older than 365 days will be automatically deleted at the end of each active monitoring day. This update is to ensure consistent and optimal performance of the monitoring app, improving your monitoring experience more efficient.

By enabling custom monitoring, you can collect monitoring data in a dedicated data pool. This dedicated monitoring data pool allows you to leverage Studio views to monitor your data pipelines.

For a video overview of how you can set up custom monitoring for your data pipeline:

And to learn more, see: Setting up custom monitoring.

The Objects and Events module is introducing a new AI-powered data modeling assistant (DMA) in private preview. This DMA uses AI to recommend a set of objects and events, along with the associated attributes and relationships, based on your intended use case and the available data. Enter your request or use case in the chat, upload any attachments such as diagrams or process flows, and then click Submit.

The DMA will use your inputs to provide an initial model that serves as the starting point for your OCDM model. You can then use the assistant’s chat functionality to adjust the recommended data model by adding, removing, or configuring the objects and events in the initial model.

You will still be able to view previous iterations of the model in the chat and return to those configurations if a change doesn’t have the expected result. Once you are satisfied with the configuration of your model, you can add the results directly into your environment by clicking the Build result button.

And for an overview of what our private and public preview releases entail, head to Feature release types.

With this private preview release, you use an AI assistant to generate configurations for custom REST API extractors from API specification files (OpenAPI, Swagger, or Postman Collection), speeding up connections to new source systems.

By uploading a JSON or YAML file, the AI assistant will identify the authentication methods and possible API endpoints, before then providing a confidence level in the connection. Then, once the extractor has been created, you can customize the connection as needed.

For a video demo of this feature:

To learn more about the Extractor Builder AI Assistant, see: Extractor Builder AI Assistant.

And for an overview of what our private and public preview releases entail, head to Feature release types.