For some data warehouse connection types, such as Snowflake and BigQuery, Metaplane is able to use metadata to observe freshness and row counts. Using metadata provides us an efficient, fast way to understand how often your data should be updated and the range of rows that we should expect to see within your given table.
In cases where we can use metadata for data points to feed into our machine-learning based monitors and if you're using the freshness and/or volume monitor types, Metaplane can show you a preview of expected values. When you do decide to enable monitor(s) for that object, you'll be able to to skip the training period associated with machine-learning models and receive alerts for incidents if/when they occur.
You'll be able to see these forecasts at the table level as shown below.
Updated 3 months ago