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dbt-utils

accepted_range

Asserts that a column's values fall inside an expected range. Any combination of min_value and max_value is allowed, and the range can be inclusive or exclusive.

USE CASE

Values range
Column
Model
dbt-core

accepted_values

Ensures that a column's values are within a predefined set of acceptable values.

USE CASE

Values range
elementary-data

all_columns_anomalies

Executes column-level monitors and anomaly detection on all columns of the table, checking data type of each column and executing relevant monitors. Excludes columns based on exclude_prefix/exclude_regexp.

USE CASE

Anomaly detection
Missing values
String format
dbt-utils

at_least_one

Asserts that a column has at least one value.

USE CASE

Completeness
dbt-utils

cardinality_equality

Asserts that values in a given column have exactly the same cardinality as values from a different column in a different model.

USE CASE

Distinct count
elementary-data

column_anomalies

Executes column-level monitors and anomaly detection on a specific column, checking the data type of the column and executing relevant monitors.

USE CASE

Anomaly detection
Missing values
String format
elementary-data

dimension_anomalies

Monitors the frequency of values in configured dimensions over time, alerting on unexpected changes in distribution. Best configured on low-cardinality fields.

USE CASE

Anomaly detection
Values distribution
dbt-utils

equal_rowcount

Asserts that two relations have the same number of rows.

USE CASE

Volume
Tables relationship
dbt-utils

equality

Asserts the equality of two relations. Optionally specify a subset of columns to compare.

USE CASE

Tables relationship
elementary-data

event_freshness_anomalies

Monitors the freshness of event data over time, focusing on the expected time it takes each event to load by measuring the difference between the event timestamp and when it is loaded to the database.

USE CASE

Anomaly detection
Freshness
dbt-expectations

expect_column_distinct_count_to_be_greater_than

Expect the number of distinct column values to be greater than a given value.

USE CASE

Distinct count
dbt-expectations

expect_column_distinct_count_to_be_less_than

Expect the number of distinct column values to be less than a given value.

USE CASE

Distinct count
dbt-expectations

expect_column_distinct_count_to_equal

Expect the number of distinct column values to be equal to a given value.

USE CASE

Distinct count
dbt-expectations

expect_column_distinct_count_to_equal_other_table

Expect the number of distinct column values to be equal to number of distinct values in another model.

USE CASE

Distinct count
Tables relationship
dbt-expectations

expect_column_distinct_values_to_be_in_set

Expect the set of distinct column values to be contained by a given set.

USE CASE

Values set
dbt-expectations

expect_column_distinct_values_to_contain_set

Expect the set of distinct column values to contain a given set.

USE CASE

Values set
dbt-expectations

expect_column_distinct_values_to_equal_set

Expect the set of distinct column values to equal a given set.

USE CASE

Values set
dbt-expectations

expect_column_max_to_be_between

Expect the column max to be between a min and max value

USE CASE

Values distribution
Values range
dbt-expectations

expect_column_mean_to_be_between

Expect the column mean to be between a min_value value and a max_value value (inclusive).

USE CASE

Values distribution
dbt-expectations

expect_column_median_to_be_between

Expect the column median to be between a min_value value and a max_value value (inclusive).

USE CASE

Values distribution

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