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Data Quality
Quality expectations attached to schema objects and properties: library checks, SQL rules, and custom assertions.
Data Quality
type: DataQuality
| Property | Type | Required | Description |
|---|---|---|---|
authoritativeDefinitions | Array | No | |
businessImpact | string | No | Consequences of the rule failure. Examples: operational, regulatory. |
Consequences of the rule failure. Examples: operational, regulatory. | |||
customProperties | CustomProperty [] | No | Additional properties required for rule execution. |
Additional properties required for rule execution. | |||
description | string | No | Describe the quality check to be completed. |
Describe the quality check to be completed. | |||
dimension | string | No | The key performance indicator (KPI) or dimension for data quality. Valid values are: accuracy, completeness, conformity, consistency, coverage, timeliness, uniqueness. |
The key performance indicator (KPI) or dimension for data quality. Valid values are: accuracy, completeness, conformity, consistency, coverage, timeliness, uniqueness. | |||
method | string | No | Examples: reconciliation. |
Examples: reconciliation. | |||
name | string | No | Name of the data quality check. |
Name of the data quality check. | |||
schedule | string | No | Rule execution schedule details. Examples: 0 20 * * *. |
Rule execution schedule details. Examples: 0 20 * * *. | |||
scheduler | string | No | The name or type of scheduler used to start the data quality check. Examples: cron. |
The name or type of scheduler used to start the data quality check. Examples: cron. | |||
severity | string | No | The severance of the quality rule. Examples: info, warning, error. |
The severance of the quality rule. Examples: info, warning, error. | |||
tags | Tags | No | |
type | string | No | The type of quality check. ’text’ is human-readable text that describes the quality of the data. ’library’ is a set of maintained predefined quality attributes such as row count or unique. ‘sql’ is an individual SQL query that returns a value that can be compared. ‘custom’ is quality attributes that are vendor-specific, such as Soda or Great Expectations. Default value is: library. Valid values are: text, library, sql, custom. |
The type of quality check. ’text’ is human-readable text that describes the quality of the data. ’library’ is a set of maintained predefined quality attributes such as row count or unique. ‘sql’ is an individual SQL query that returns a value that can be compared. ‘custom’ is quality attributes that are vendor-specific, such as Soda or Great Expectations. Default value is: library. Valid values are: text, library, sql, custom. | |||
unit | string | No | Unit the rule is using, popular values are rows or percent, but any value is allowed. Examples: rows, percent. |
Unit the rule is using, popular values are rows or percent, but any value is allowed. Examples: rows, percent. | |||
Specific Type Properties & Types
library( See DataQualityLibrary )sql( See DataQualitySql )custom( See DataQualityCustom )
Data Quality Checks
type: DataQualityChecks
Data quality rules with all the relevant information for rule setup and execution. Implemented as array of type DataQuality [].
Data Quality Library
type: DataQualityLibrary
| Property | Type | Required | Description |
|---|---|---|---|
mustBe | No | Must be equal to the value to be valid. When using numbers, it is equivalent to ‘=’. | |
Must be equal to the value to be valid. When using numbers, it is equivalent to ‘=’. | |||
mustBeBetween | number[] | No | Must be between the two numbers to be valid. Smallest number first in the array. |
Must be between the two numbers to be valid. Smallest number first in the array. | |||
mustBeGreaterOrEqualTo | number | No | Must be greater than or equal to the value to be valid. It is equivalent to ‘>=’. |
Must be greater than or equal to the value to be valid. It is equivalent to ‘>=’. | |||
mustBeGreaterThan | number | No | Must be greater than the value to be valid. It is equivalent to ‘>’. |
Must be greater than the value to be valid. It is equivalent to ‘>’. | |||
mustBeLessOrEqualTo | number | No | Must be less than or equal to the value to be valid. It is equivalent to ‘<=’. |
Must be less than or equal to the value to be valid. It is equivalent to ‘<=’. | |||
mustBeLessThan | number | No | Must be less than the value to be valid. It is equivalent to ‘<’. |
Must be less than the value to be valid. It is equivalent to ‘<’. | |||
mustNotBe | No | Must not be equal to the value to be valid. When using numbers, it is equivalent to ‘!=’. | |
Must not be equal to the value to be valid. When using numbers, it is equivalent to ‘!=’. | |||
mustNotBeBetween | number[] | No | Must not be between the two numbers to be valid. Smallest number first in the array. |
Must not be between the two numbers to be valid. Smallest number first in the array. | |||
rule | string | Yes | Define a data quality check based on the predefined rules as per ODCS. Examples: duplicateCount, validValues, rowCount. |
Define a data quality check based on the predefined rules as per ODCS. Examples: duplicateCount, validValues, rowCount. | |||
Data Quality SQL
type: DataQualitySql
| Property | Type | Required | Description |
|---|---|---|---|
query | string | Yes | Query string that adheres to the dialect of the provided server. Examples: SELECT COUNT(*) FROM ${table} WHERE ${column} IS NOT NULL. |
Query string that adheres to the dialect of the provided server. Examples: SELECT COUNT(*) FROM ${table} WHERE ${column} IS NOT NULL. | |||
Data Quality Custom
type: DataQualityCustom
| Property | Type | Required | Description |
|---|---|---|---|
engine | string | Yes | Name of the engine which executes the data quality checks. Examples: soda, great-expectations, monte-carlo, dbt. |
Name of the engine which executes the data quality checks. Examples: soda, great-expectations, monte-carlo, dbt. | |||
implementation | string, object | Yes | |
Last updated on November 1, 2026