Manage Checks¶
Alation Data Quality provides a no-code interface for configuring data quality checks across multiple categories. All checks are executed as SQL queries on the data using the Connector’s Query Service and return a status of Pass, Fail, or Error.
A data quality check is a rule applied to a table or column that evaluates whether the data meets an expected condition and within the defined threshold value.
A threshold is the expected value or range of values that a data quality metric must satisfy during a check. If the observed data meets the threshold, the check passes; if it violates the threshold, the check fails.
Comparison Operators¶
Operator |
Meaning |
---|---|
= |
Equal to |
< |
Less than |
> |
Greater than |
<= |
Less than or equal to |
>= |
Greater than or equal to |
!=, <> |
Not equal to |
between |
Value is within a specified range |
not between |
Value is outside a specified range |
Each check is designed to detect specific types of data quality issues such as missing values, invalid formats, duplicate records, or outdated timestamps.
Result |
Meaning |
Contribution to Score |
---|---|---|
Pass |
The data meets the check’s condition |
Positive |
Fail |
The data violates the check’s condition |
Negative |
Error |
The check failed to execute due to a syntax or runtime issue |
Negative |
The data quality checks are grouped into two main categories:
Table-level check: Includes numerical and custom SQL query check.
Column-level check: Includes numerical, uniqueness, completeness, validity, and custom (common table expressions and SQL query) checks.
Data Quality Check Types by Column Data Type¶
Check Category & Metric |
Numerical |
Text |
Time/Date |
Table Level |
---|---|---|---|---|
ACCURACY CHECKS |
||||
Average ( |
Yes |
— |
— |
— |
Average Length ( |
— |
Yes |
— |
— |
Maximum ( |
Yes |
— |
— |
— |
Minimum ( |
Yes |
— |
— |
— |
Maximum Length ( |
— |
Yes |
— |
— |
Minimum Length ( |
— |
Yes |
— |
— |
Percentile ( |
Yes |
— |
— |
— |
Standard Deviation ( |
Yes |
— |
— |
— |
Standard Deviation Population ( |
Yes |
— |
— |
— |
Standard Deviation Sample ( |
Yes |
— |
— |
— |
Sum ( |
Yes |
— |
— |
— |
Variance ( |
Yes |
— |
Yes |
— |
Variance Population ( |
Yes |
— |
Yes |
— |
Variance Sample ( |
Yes |
— |
Yes |
— |
Row Count ( |
— |
— |
— |
Yes |
UNIQUENESS CHECKS |
||||
Duplicate Count ( |
Yes |
Yes |
Yes |
— |
Duplicate Percentage ( |
Yes |
Yes |
Yes |
— |
COMPLETENESS CHECKS |
||||
Missing Count ( |
Yes |
Yes |
Yes |
— |
Missing Percentage ( |
Yes |
Yes |
Yes |
— |
VALIDITY CHECKS |
||||
Invalid Count ( |
Yes |
Yes |
Yes |
— |
Invalid Percentage ( |
Yes |
Yes |
Yes |
— |
TIMELINESS CHECKS |
||||
Freshness ( |
— |
— |
Yes |
— |
CUSTOM CHECKS |
||||
Common Table Expression ( |
Yes |
Yes |
Yes |
— |
SQL Query ( |
Yes |
Yes |
Yes |
Yes |