When in the Airflow application and adding a connection back to Monte Carlo:

• First, install the airflow provider: https://pypi.org/project/airflow-mcd/
• Then, you can now select the "Monte Carlo Data" connection type. This allows you to use the new test feature in Airflow to confirm that their token is valid, service user permissions are correct, and that Monte Carlo is reachable when using circuit breakers or our Airflow observability and incidents integration.

Field health anomalies on numeric fields like mean, percentiles, etc. will now also have correlation insights to assist the root cause analysis. The insights will visualize the impact on mean, percentile metrics from both the row count and field value of the anomalous rows.

Now you can see Airflow DAGs as an Asset, and have Monte Carlo trigger an incident when a DAG fails.

To get Asset pages for Airflow DAGs, annotate your queries as described in: https://docs.getmontecarlo.com/docs/airflow-lineage-and-assets

And then to get Incidents from Airflow failures, add callbacks as described in: https://docs.getmontecarlo.com/docs/airflow-incidents-dags-and-tasks

In notification settings, you’ll now see a counter showing how many notifications were sent to that recipient in the last 30 days, along with a 30 day trendline showing notifications sent day-over-day.

This makes it much easier to measure how many notifications are being sent where, so that customers can prevent alert fatigue.

When creating a SQL Rule, the SQL Editor now includes a helpful search feature with auto-complete support for fields and tables. To initiate the autocomplete suggestions, simply begin typing a few words of your desired search and press ctrl+space.

between two different tables/fields. Some users call this ‘source to target.’ Click here to add a Comparison Rule in Monte Carlo.

This monitor type is currently limited making comparisons between two tables in a single source (e.g. Snowflake), but may be extended to compare tables and fields across multiple sources (e.g. compare a metric from Snowflake to a metric in Postgres).

Like Cardinality Rules and Referential Integrity Rules, this is a no-code interface that will then produce a SQL Rule.

Dashboard creators can now share their Data Product Dashboards within an org for better collaboration and accountability on the health of Data Products.

To share, select the "Share data product" icon in the top right when viewing a Data Product Dashboard.

See full documentation

Data Product Dashboards allow users to get an overview of the data quality across a selected list of assets that make up a Data Product. This dashboard enables you to build trustworthy data products by providing visibility into critical data asset health and reliability.

Start by going to Dashboards -> Data Product in Monte Carlo. In the top right, select "Create Dashboard".

See full documentation

A few impactful quality-of-life improvements have been made to the details drawer in the Performance dashboard:

1. The Volume chart now has the ability to toggle between Total Rows, Total Rows by Change, and Total Bytes.
2. The queries table in the details drawer now dynamically updates its rows to reflect the lookback window specified in the runtime and volume charts above.
3. The Volume chart now has a zoom slider and syncs with the runtime chart

GitHub Pull Requests are now visually overlaid on incident charts for volume / freshness incidents, as well as on volume charts in Assets. This helps users easily correlate the timing of pull requests with anomalies to speed up incident resolution.