Validator Types
Overview of supported Validator types used for calculating metrics.
Validio supports different types of validators organized by the different use cases you want to monitor, such as pipeline health, data consistency, completeness, and so on. You can also create a validator using SQL queries to monitor custom metrics. This guide lists the different validator types and supported metrics for each validator.
Validator Metrics and Windows
Validators calculate metrics over a window. For example, a validator calculates the mean value over a daily window, and then validates if these daily mean values follow an expected seasonal pattern. The About Validators and Configuring Validators guides explain the concepts related to validators and provide useful context for configuration.
Metadata Validators for Data Warehouse Sources
Metadata validators, Freshness (metadata) and Row Count (metadata), are only available for BigQuery and Snowflake sources.
Pipeline Health
Evaluate data pipeline reliability to identify issues in ingestion or processing by monitoring row counts and freshness.
| Validator Type | Description | Metric |
|---|---|---|
| Freshness | Ensure data is timely by checking if its timestamp is within the expected range. | Freshness |
| Row Count | Verify the number of rows in a table meets expected thresholds. | Count |
| Freshness (metadata) | Ensure data is timely by checking if the table was last updated within the expected time range. This check is based on the warehouse metadata. | Freshness |
| Row Count (metadata) | Ensure the row count in the table is within the expected range. This check is based on the warehouse metadata. | Count |
Uniqueness
Maintain quality standards by checking for duplicate or distinct values in specific fields.
| Validator Type | Description | Metric |
|---|---|---|
| Distinct Values | Ensure a column contains only distinct values or matches expected uniqueness. | Unique Count, Unique Percentage |
| Duplicate Values | Identify duplicate entries in a column to maintain data integrity. | Duplicate Count, Duplicate Percentage |
Completeness
Ensure datasets meet completeness requirements by checking for null values, empty strings, or missing data.
| Validator Type | Description | Metric |
|---|---|---|
| Null Values | Check for null values to ensure data completeness and reliability. | Count, Percentage |
| Empty Strings | Check a specific field for empty string values to maintain validity. | Count, Percentage |
| Enum Values | Ensure a field matches a predefined set of allowed values. | Count, Percentage |
Metrics & Validity
Evaluate numeric and categorical data to verify expected patterns using metrics such as minimum, maximum, mean, and distribution shift.
Validator Type | Description | Metric |
|---|---|---|
Check a numeric field against metrics like maximum, minimum, mean, or sum. | Mean Maximum Minimum Standard Deviation Sum | |
Check if a numeric field’s values match the expected distribution, comparing two datasets. | Relative Entropy Mean Ratio Maximum Ratio Minimum Ratio Standard Deviation Ratio | |
Ensure a categorical field’s values match expected proportions, comparing two datasets. | Categories Added Categories Removed Categories Changed Relative Entropy | |
Check data volume metrics like count, percentage, duplicates, or distinct values. | Count Percentage Duplicate Count Duplicate Percentage Unique Count Unique Percentage | |
Compare the time difference between two data subsets. | Minimum Difference Maximum Difference Mean Difference | |
Compare the volume between two data subsets. | Count Ratio Percentage Ratio |
Custom
Define and validate custom metrics.
| Validator Type | Description | Metric |
|---|---|---|
| Custom SQL | Use SQL queries for tailored validation of metrics and conditions. | Custom |
Reference Validators
Validators are either single source or reference. Single source validators calculate metrics based on one dataset, while reference validators calculate metrics based on multiple fields from two different datasets. Reference validators (such as Numeric Distribution, Relative Time, Relative Volume, and Categorical Distribution) only calculate metrics if there is data in the target dataset.
For example, a Categories Removed validator where the reference dataset has 4 categories and the target dataset has 3 categories, yields a result of 1. If the target dataset has 0 categories, the validator does not return any result, because the target dataset has no data to calculate metrics on.
Updated 3 months ago