Investigating data issues just got easier with invalid row sample profiling. Quickly identify correlations with at-a-glance statistics and top values. Drill in deeper, copy entire columns directly to your clipboard, customize views by column selection and sorting, or download rows as a CSV for offline analysis.
Now available in public preview—rolling out to accounts over the next week. If you have feedback or requests on this, we'd love to hear more.
We've added support for bidirectional synchronization for alert priority, owner, status, and comments between Monte Carlo and Opsgenie so that users no longer have to reconcile the information manually.
A user with this role has all the same privileges as an Editor, except they cannot:
This means that a Monitor Editor can create monitors like Metrics and Validations, but cannot add Table Monitors that may contribute to your Monte Carlo bill. See docs for more details.
The section in Settings formerly called "Usage" has been renamed "Table Monitors". Users go here to manage which assets are ingested and which have table monitors applied.
This is a cosmetic change and does not impact any underlying functionality.
Users can now apply tags to ETL jobs (Airflow DAGs, Databricks workflows, Azure Data Factory pipelines, dbt jobs), similar to how they add tags for tables today. MC also automatically imports external job tags from Airflow DAGs, Databricks workflows, Azure Data Factory into MC. For Airflow tags import, python package airflow-mcd 0.3.6 or above is required
This gives users flexibility to organize their job assets same way as tables, such as bulk adding jobs to audience, domains using tags.
We are removing the old "API" section from the Settings page. "API keys" is now under "Data and integrations" section, and "API explorer" is now under user menu.
MC now supports Snowflake OAuth via Snowflake External OAuth. Customers can update existing Snowflake connection to use OAuth and set up new connection using OAuth. Docs here.
We've implementing some changes around how an Asset looks when it is not currently enabled for monitoring by Monte Carlo. Users now will be able to access more card on the Summary page, access the Monitors tab as well as run the Data Profiler for that table.
In addition, we've added other helpful way to get unmonitored tables enabled for monitoring. If you have access to the Usage UI, clicking "Enable" on an unmonitored table will provide a list of suggested ways you could get a table enabled for monitoring. Selecting one of these options will automatically add a rule in the Usage UI and enable the table for monitoring.
We've dramatically improved the anomaly detection for "Time since last row count change" in Metric Monitors. This enables customers to do more sensitive freshness monitoring for highly segmented use cases.
The model now produces more sensitive thresholds, and generates thresholds faster as well.
For example, this would be very helpful if you have multiple versions of an app, each sending events back to the same table, and you wanted to be alerted if any version went abnormally long without sending any events.
We've improved our rule in the Usage UI to let users include/exclude tables by last activity. Now, users can specify "read", "write" or "read or write" activity and select from 7 to 31 days for lookback.
As an example, this rule could be used to include all tables from a certain schema with recently changed data (recent write activity).
For more details on the Usage UI, refer our docs
