Templates Library

Monitors can be set both on a table or a field level in order to control specific Data Quality criteria. They can be run manually or automatically based on a Schedule, and they can alert users in case of a breach (see: Notifications Setup).

Below, you'll find a Library of predefined Sifflet Monitor Templates.

Type	Monitor	Application	Description
Table-level Health	Volume	Table	Counts newly ingested rows and alerts on anomalous behaviour
Table-level Health	Row-level Duplicates	Table	Computes the duplication rate [%] on a row-level and compares it to the expected value based on past behavior.
Table-level Health	Freshness	Table	Verifies whether new rows have been ingested into your table following the expected pattern.
Table-level Health	Freshness (Update time gap)	Table	The monitor fails when the duration since the last update deviates from historical norms.
Table-level Health	Schema Change	Table	Detects any change to the schema: new field(s), removed field(s), existing field(s) with updated types or names.
Metrics	Metrics	Fields: Numeric	The monitor detects changes in an aggregated metric of a field (i.e. Sum)
Metrics	Custom Metrics	Table	The rule fails if the time series returned by the query behave differently than they did in the past
Metrics	Correlated Metrics	Fields: Numeric	The monitor fails if defined metrics diverge significantly from each other.
Field profiling	Distribution Change	Fields: All	The monitor fails if the distribution of a given field has changed abnormally compared to a former given run.
Field profiling	Duplicates	Fields: All	The monitor detects anomalies regarding the count of duplicates for a column or a set of columns
Field profiling	Unique	Fields: All	A simplified version of the duplicates monitor that fails if a column or set of columns is not unique
Field profiling	Nulls	Fields: All	The monitor detects anomalies regarding the count of nulls/empties in a column or a set of columns
Field profiling	Value List Validation	Fields: String	The monitor fails if the chosen field has values that are not present in the given list.
Field Profiling	Value Range	Fields: Numeric	The monitor fails if the chosen field has any values outside of a given range.
Field profiling	Referential Integrity	Fields: All	The monitor fails if values in one table are not present in the other table
Format validation	Is an email	Fields: String	The monitor fails if the chosen field contains at least one row that does not have an email format.
Format validation	Is a phone number	Fields: String	The monitor fails if the chosen field contains at least one row that does not have a phone number format. I.e. checking for 6 to 16 digits and will accept the field to contain the following characters +, -, (, )
Format validation	Is UUID	Fields: String	The monitor fails if the chosen field contains at least one row that does not have a UUID format.
Format validation	Matches regex	Fields: String	The rule fails if the selected field contains at least one row that does not match the format specified by the given regular expression.
Custom	SQL	Table	Advanced template to write custom monitors based on business specifics. The SQL query must describe a quality breach on one or more tables within the same data source.
Custom	Conditional monitors	Tables	As for SQL, this template allows to write custom monitors based on business use cases. With conditional statements, no SQL syntax is needed. The rule fails if values are found inside the filtering criteria set by conditional rules