Once you've added some data assets, you can create metrics to measure their data quality. There are three classes of metrics: metadata metrics, autometrics, and deep metrics.
Metadata metrics measure a table's characteristics: how big it is, when the most recent write occurred, etc. Unlike deep metrics, metadata metrics don't query your data so they incur fewer costs. This makes them ideal for broad monitoring of table reliability, so you can identify the right data assets for creating deep metrics.
For steps and more information, see Metadata metrics.
Autometrics are pre-configured metrics that measure basic data quality of assets. There are six types of autometrics: Table activity (for schemas), Data Delay and Data volume (for tables), and Category activity, Distribution, and Null Percent (for columns). Because they are pre-built you can just enable them, but you can also create deep metrics that are based on autometrics (except for Activity autometrics).
For steps and more information, see Autometrics.
Deep metrics are metrics that you create to answer specific data quality questions. Typically, you create deep metrics after establishing which assets to focus on, through data profiling, autometrics, and metadata metrics.
For steps and more information, see Deep metrics.
Updated about 21 hours ago