HomeDocumentationRecipesChangelog
HomeRequest DemoContact
Documentation
HomeRequest DemoContact

Volume

Monitor number of records, non-NULL values, duplicates, unique values, and so on.

Validator Overview

You can configure volume validators to monitor the following metrics:

Metric OptionsDescription
CountValidates the number of total rows.
PercentageValidates the percentage of rows passing certain filter criteria.
Duplicate CountValidates the number of duplicates.
Duplicate PercentageValidates the percentage of duplicates.
Unique CountValidates the number of unique rows.
Unique PercentageValidates the percentage of unique rows.

Field selection

For metrics Duplicates count, Duplicates percentage, Unique count and Unique percentage you have to specify one or several fields. Specifying several fields means field values are concatenated before being counted, which is, for example, useful when validating uniqueness of composite keys.

For metrics Count and Percentage you can pick No field (use record) to count entire records. You can also pick a specific field to count non-NULL values in that field.

๐Ÿ“˜

Note

For duplicate and unique metrics, Validio includes NULL values in the counts, unless you apply a NULL filter to remove them.

Metric Configuration Parameters

Parameters

Description

Options

Metric

Select the metric to calculate.

Count
Percentage
Duplicate count
Duplicate percentage
Unique count
Unique percentage

Field

Select a source field to use for the calculation.

List of available fields with numeric data types.

Filter

(Optional) Use filters to specify which records to include in the calculation.

List of existing filters or create a new filter.

Window

Use windows to define the time-range over which the data is aggregated.

List of existing windows or create a new window.

Segmentation

Use segmentation to break the data into separate groups for analysis.

List of existing segmentations, Unsegmented (default), or create a new segmentation.

Initialize using historic data

Start the validator with historical data to prime the anomaly detection algorithms.