Monitors library

A monitor can be set on either a table or a field to check specific data quality criteria. Monitors can be scheduled to run based on a flexible scheduler and can alert the user in case of a breach on Slack or by email.

Find below the available Monitors with a brief description of what they do.

Type	Monitiors	Applicable To	Description
Metadata	Completeness (ML-Based)	Table	Counts the new ingested rows and compares them to expectations based on past behavior.
Metadata	Duplicates (ML-Based)	Table	Computes the duplication rate on row-level and compares it to expectations based on past behavior.
Metadata	Freshness (ML-based)	Table	Detects the frequency of your table new rows ingestion.
Metadata	Schema Change	Table	Detects any new change to the schema: new field(s), removed field(s), existing field(s) with updated types or names.
Metrics	Average (static thresholds)	Fields: Numeric	The monitor fails if the average of the field is outside of a given range
Metrics	Values count (static thresholds)	Fields: All	The monitor fails if the number of unique values of the field is outside of a given range
Metrics	Quantile (static thresholds)	Fields: Numeric	The monitor fails if a quantile of the field is outside of a given range.
Metrics	Values (static thresholds)	Fields: Numeric	The monitor fails if the chosen field has one or more values outside of a given range.
Metrics	Standard Deviation (static thresholds)	Fields: Numeric	The monitor fails if the standard deviation of the field is outside of a given range.
Metrics	Variance (static thresholds)	Fields: Numeric	The monitor fails if the variance of the field is outside of a given range.
Smart Metrics	Interlinked Metrics	Fields: Numeric	The monitor fails if the defined metrics diverge significantly from each other
Smart Metrics	Metrics (dynamic thresholds)	Fields: Numeric	The monitor fails is the selected statistical transformation of the field behaves differently than it did in the past.
Smart Metrics	Metrics Custom (dynamic thresholds)	Table	The rule fails if the time series returned by the query behave differently than they did in the past
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 in % (static thresholds)	Fields: All	The monitor fails if chosen field duplicate rate is superior to a given threshold.
Field profiling	Duplicates in # (dynamic thresholds)	Fields: All	The monitor fails if the count of duplicate values of the field is abnormal compared to expectations based on past behavior.
Field profiling	Duplicates in % (dynamic thresholds)	Fields: All	The monitor fails if the % of duplicate values of the field is abnormal compared to expectations based on past behavior.
Field profiling	Low Cardinality	Fields: All	The monitor fails if: - The chosen field has several different values above a given threshold; - The different values of the field changed since the previous run Ex: from ['dog', 'cat'] to ['dog', 'rabbit', 'turtle'].
Field profiling	Not after date	Fields: Timestamps, Dates	The monitor fails if the table has rows after a given date.
Field profiling	Not before date	Fields: Timestamps, Dates	The monitor fails if the table has rows before a given date.
Field profiling	Not in the list	Fields: String	The monitor fails if the chosen field has values that are not present in the given list.
Field profiling	Null in # (static thresholds)	Fields: All	The monitor fails if the chosen field has values that are empty/null.
Field profiling	Null in # (dynamic thresholds)	Fields: All	The monitor fails if the count of null values of the field is abnormal compared to expectations based on past behavior.
Field profiling	Null in % (dynamic thresholds)	Fields: All	The monitor fails if the % of null values of the field is abnormal compared to expectations based on past behavior.
Field profiling	Unique	Fields: All	The monitor fails if the chosen field has duplications.
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.
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 rules	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