not_null
, unique
, accepted_values
, relationship
)dbt-utils
and dbt-expectations
.dbt_utils.recency
, or dbt source freshness.event_freshness_anomalies
to detect anomalies.expect_table_row_count_to_be_between
.dimension_anomalies
, that will count rows grouped by a column or combination of columns and can detect drops or spikes in volume in specific subsets of the data.accepted_values
. If you also expect a consistency in ratio of these values, use dimension_anomalies
and group by this column.expect_column_values_to_be_between
, expect_column_values_to_be_increasing
, expect-column-values-to-have-consistent-casing
where
clause in the tests, and only validate recent data. This will prevent running the tests on large data sets which is costly and slow.
unique
and not_null
tests to key columnsTest Coverage
page in Elementary allows adding any dbt test from the ecosystem, Elementary anomaly detection monitors, and custom SQL tests. We are working on making it easier to add tests by creating a test catalog organized by quality dimensions and common use cases.
Example for tests in each quality dimension -