Custom monitor failure notifications due to misconfigurations used to be limited to a frequency of 1 notification per week. We've changed the frequency to up to 1 notification per day in order to reduce the risk of monitor failing silently while still being mindful of alert fatigue.

We’ve improved our automated freshness monitor to better handle weekly patterns. We've sometimes heard customers refer to these as “bimodal” thresholds… meaning one threshold during weekdays, and a different threshold during weekends. These are valuable when the table updates during the week, but not (or less frequently) on the weekend.

Specific changes:

  • We more accurately recognize weekends. We previously had hardcoded support for Sat/Sun weekends. Now, we dynamically recognize weekends regardless of where they fall in the week (e.g. Fri/Sat, or when there’s just a single day “off”)
  • Tighter thresholds in the “active” period of the week. About 5% of tables that we’re tracking have had their thresholds reduced by >25% from this change!
  • Thoughtful thresholds in the “off” part of the week. So if the table doesn’t start back up on Monday, we’ll alert.

In the future, we’ll extend these improvements to “Time since last row count change”, and also add bimodality for day/night behaviors.

MC now supports Databricks oAuth M2M authentication with setup available via the CLI (UI to come).
Follow docs here to authenticate your MC Service Principal via M2M.

Next week, the UI of the login page will be refreshed. All functionality, links, and workflows from the login page remain the same. The changes are cosmetic only.

New login page

New login page

We are about to remove the two widgets at the top of the monitors page as the information they present appears in other dashboards in the product. This is the first step in our overhaul of the monitor list to improve usability and simplify monitor management workflows.

MC can now manage failures from Databricks Workflows. This helps users triage and resolves all data and system issues in one single platform. Follow docs here to set up the webhook connection to start alerting.

Azure Data Factory pipeline failures can now be managed via MC. This helps users centrally detect, triage and resolve Data Factory pipeline issues along with all other data incidents within MC. Follow docs here to start using.