Autometrics allow you to immediately start monitoring common data quality metrics such as data volume, data delay, null percent, and schema changes. You can enable them with one click when you add a data asset to Explorer (a schema, a table, or a column).
Lightup's autometrics exist at the schema, table, and column level and are listed below. Note that custom versions of these can also be created - for example, if you want to slice the metric or apply a where clause. These toggleable autometrics simply give you a very quick way to bootstrap your data quality journey with some common and useful metrics.
|Autometric type||Data asset type||What it measures|
|Table Activity||Schema||Measures tables added or dropped from a schema|
|Column Activity||Table||Measures columns added, altered (data type), or dropped from a table|
|Data delay||Table||Delays in the arrival of expected data into data assets (a measure of timeliness).|
|Data volume||Table||Deviations from expected data volumes (a measure of completeness).|
|Distribution||Column||Changes in the distribution of values.|
|Null percent||Column||Changes in the percent of values that are null|
|Category Activity||Column||Measures categories added or dropped from a column|
Since most autometrics are at the table and column level, you will first have to configure the table. For details, view Configure your table for data quality analysis. The one exception is the schema level Activity metric, which tells you about changes in the tables in your schema. This can be turned on as soon as the schema is made active.
Updated about 1 month ago