From the /monitors tab, users can now select multiple monitors at once and then apply an audience to them. This simplifies the process of adding a new audience to many monitors, making it easier for you to route notifications to the right place.

Today, we are releasing Performance Monitors to the Monte Carlo UI, which has been one of the most popular requests since the Performance Dashboard's creation! With this feature you'll be able to set up monitors that can solve use cases such as:

• Getting alerted if any query from a warehouse/user/database has a runtime that is X% longer than normal
• Getting alerted if any query from a warehouse/user/database has a runtime that is longer than an absolute threshold
• Getting alerted if an important query failed
• And many more!

If a user is making a change to a custom monitor that might significantly alert its results, the user is now prompted if they want to retrain their anomaly detection models or not. Previously, Monte Carlo would automatically begin a retrain, and there were some cases where this was not desirable (e.g. a small change in the filter).

Users are only prompted with this modal if they are making a significant change to the SQL, the schedule, segmentation, editing variables, etc. Editing a field like notes, description, or audience will not prompt this modal.

Today we are enabling the ability to store the Monte Carlo sampling data in your cloud Azure. This is a new deployment option to store the Object Storage bucket directly in your cloud environment. This is in addition to already being available for both for AWS (S3) and Google Cloud (GCS).

More information on Deployment Options is now available in the documentation.

Today we are announcing Monte Carlo's foray into data observability for Oracle, starting with the ability to run SQL Rules and detect Volume anomalies. SQL Rules can be created in either the UI and/or programmatically via Monitors-as-Code (API/SDK too). These monitors can be used to generate notifications to relevant stakeholders, circuit break pipelines, and conduct RCA (e.g. sampling). Asset entries are also created in Monte Carlo and Custom Lineage APIs are available to write lineage across databases to data warehouses.

Monte Carlo now supports deploying a remote Agent in Google Cloud for customers to establish connectivity between Monte Carlo and their resources from Google Cloud. Monte Carlo embraces transparency for customer security; the Agent is publicly accessible in Docker Hub and the Terraform Registry. Yes, customers can now deploy the Monte Carlo Agent using Terraform (on Google Cloud - AWS and Azure to come). The Agent provides an audit log of all agent operations and includes a change log for each release. Get started with the docs or reach out to Monte Carlo to deploy using the Google Cloud Agent.

Today we are announcing Monte Carlo's foray into data observability for Teradata, starting with the ability to run SQL Rules and detect Volume anomalies. SQL Rules can be created in either the UI and/or programmatically via Monitors-as-Code (API/SDK too). These monitors can be used to generate notifications to relevant stakeholders, circuit break pipelines, and conduct RCA (e.g. sampling). Asset entries are also created in Monte Carlo and Custom Lineage APIs are available to write lineage across databases to data warehouses.

A brand new version of the incident feed has been released to all customers with the primary goal of giving users a better overview of the incidents that are surfaced in Monte Carlo. Some of the top changes and additions include:

• Table-based format: Switched from a card-based layout to a table-based layout with expandable rows to surface incidents.
• Bulk Updates: You are now able to update the status, owner, and severity of multiple incidents in one action by utilizing the check boxes to the left of each row and then dropdown buttons at the top of the page.
• Date Ranges: You can now view incidents with a start and end date by either using the presets in the dropdown menu or a custom date range of up to two months using the “custom” button.
• User Activity Section: You are now able to see and comment in the User Activity section for any incident directly from the feed.

This feature is going through a phased rollout to customers -- it is currently available in some but not all customer environments.

Notifications 2.0 is a set of improvements that make it easier to set up, manage, and maintain notifications in Monte Carlo. Key improvements:

• An audience is now an easy-to-create and easy-to-edit group of 1 or more recipients
• All kinds of notifications (custom monitors, automated monitors, digests, etc) map back to an audience
• It’s easy to create an audience on-the-fly as part of custom monitor creation

These improvements do not introduce any major new concepts. Instead, they just remove friction from the current experience. Learn more in our docs. For some customers, moving to Notifications 2.0 will require migrating notification settings.

When parameterizing values from breached rows into notifications, we now will show up to 50 values or 2,000 characters in alerts, whichever is reached first. The previous limits were 10 and 500, respectively.