Skip to main content
The default freshness and volume monitors have been merged into the Table stats monitor. You can see more on the Table stats monitor page.
The freshness monitor looks at a table’s timestamp and alerts you if it’s been too long since the last update. Freshness monitors rely on your data warehouse metadata tables for timestamp information (e.g., Snowflake table information schema). As the monitor queries the information schema, you only incur minimal additional costs even when they are run frequently. The monitor works out of the box, which makes it easy to deploy at scale. title
In most cases, we recommend combining freshness monitors with default Volume monitor to detect when data is flowing but at a reduced rate. Read more about best practices for deploying these at Setting up monitors
Default freshness monitors are run every 60 minutes.

​
Setting up a freshness monitor

  1. Navigate to Health → Manage monitors
  2. Click Create monitor group to define the tables you want to monitor
  3. Use the SYNQ browser to narrow down the tables you want to monitor
title
  • Browser—select specific schemas or search for tables to monitor (SYNQ automatically maps your data warehouse tables and dbt models)
  • Annotation—select assets with metadata definitions such as tag defined in a yml file from dbt
  • Important—select assets that you’ve marked as important
  • Query—advanced selection. E.g., search for specific keyword matches
To deploy freshness monitors on all dbt sources automatically, filter by Type: Source
  1. Check Freshness to set up a freshness monitor group
title
  1. Name the monitor (e.g., freshness on all sources).
title
  1. Click continue to set up the monitor
You’ll be able to see the monitors on the Health overview page and adjust the monitor sensitivity.
As soon as you’ve set the monitor up, it will start learning the behaviors of your data every hour. You should expect ~10 days before the monitor has built an understanding of the seasonality and patterns in your data and is confidently able to predict anomalies