Last updated: July 05, 2025
How to Detect Invalid UUID Values Using Data Quality Checks? Examples
This sample shows how to use data quality checks to measure the percentage of valid UUID values in a column and view the results on data quality dashboards.
Overview
The following example shows how to verify that the percentage of valid UUID values in a column does not fall below the minimum accepted percentage.
PROBLEM
Here is a table with some sample customer data. The uuid
column contains UUID data.
We want to verify the percent of valid UUID on uuid
column does not fall below the set threshold.
SOLUTION
We will verify the data using monitoring invalid_uuid_format_percent column check.
Our goal is to verify that the percent of invalid UUID values in a uuid
column does not exceed the set threshold.
In this example, we will set the minimum percent thresholds levels for the check:
- error: 5%
If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.
VALUE
If the percentage of invalid UUID values exceeds 5%, an error alert will be triggered.
Data structure
The following is a fragment of the DQOps
dataset.
The uuid
column of interest contains both valid and invalid UUID values.
uuid | result | date |
---|---|---|
26x5e2be-925b-11ed-a1eb-0242ac120002 | 0 | 2/12/2023 |
wrong UUID | 0 | 5/15/2022 |
26b5e2be-925b-112ed-a1eb-0242ac120002 | 0 | 3/13/2022 |
2137 | 0 | 6/16/2022 |
26b5d9a4-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5c586-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5dd64-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5dc24-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5cdc4-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5e2be-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5cc84-925b 11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5d5ee-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5ca7c 925b 11ed a1eb 0242ac120002 | 1 | 1/11/2023 |
26b5d486-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5df9e-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5d21a-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5e124-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5daf8-925b-11ed-a1eb-0242ac120002 | 1 | 1/11/2023 |
26b5c8b0925b11eda1eb0242ac120002 | 1 | 1/11/2023 |
Run the example using the user interface
A detailed explanation of how to start DQOps platform and run the example is described here.
Navigate to a list of checks
To navigate to a list of checks prepared in the example using the user interface:
-
Go to the Monitoring section.
The Monitoring Checks section enables the configuration of data quality checks that are designed for the daily and monthly monitoring of your data source.
-
Select the table or column mentioned in the example description from the tree view on the left.
On the tree view you can find the tables that you have imported. Here is more about adding connection and importing tables.
-
Select the Daily checkpoints tab.
This tab displays a list of data quality checks in the check editor. Learn more about navigating the check editor.
Run checks
Run the activated check using the Run check button.
You can also run all the checks for an entire subcategory of checks using the Run check button at the end of the line with the check subgroup name.
View detailed check results
Access the detailed results by clicking the Results button. The results should be similar to the one below.
Within the Results window, you will see four categories: Check results, Sensor readouts, Execution errors, and Error sampling. The Check results category shows the severity level that result from the verification of sensor readouts by set rule thresholds. The Sensor readouts category displays the values obtained by the sensors from the data source. The Execution errors category displays any error that occurred during the check's execution. The Error sampling category displays examples of invalid values in the column.
The actual value in this example is 25%, which is above the minimum threshold level set in the error (5.0%). The check gives an error (notice the orange square to the left of the check name).
Synchronize the results with the cloud account
Synchronize the results with your DQOps cloud account using the Synchronize button located in the upper right corner of the user interface.
Synchronization ensures that the locally stored results are synced with your DQOps Cloud account, allowing you to view them on the dashboards.
YAML configuration file
The YAML configuration file stores both the table details and checks configurations.
In this example, we have set the maximum percent threshold level for the check:
- error: 5
The highlighted fragments in the YAML file below represent the segment where the monitoring daily_invalid_uuid_format_percent
check is configured.
If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.
apiVersion: dqo/v1
kind: table
spec:
incremental_time_window:
daily_partitioning_recent_days: 7
monthly_partitioning_recent_months: 1
columns:
uuid:
type_snapshot:
column_type: STRING
nullable: true
monitoring_checks:
daily:
patterns:
daily_invalid_uuid_format_percent:
error:
max_percent: 5.0
result:
type_snapshot:
column_type: INT64
nullable: true
date:
type_snapshot:
column_type: DATE
nullable: true
In this example, we have demonstrated how to use DQOps to verify the validity of data in a column. By using the invalid_uuid_format_percent column check, we can monitor that the percentage of valid UUID values in a column does exceed the maximum accepted percentage. If it does, you will get an error result.
Next steps
- You haven't installed DQOps yet? Check the detailed guide on how to install DQOps using pip or run DQOps as a Docker container.
- For details on the valid_uuid_format_percent check used in this example, go to the check details section.
- You might be interested in another validity check that evaluates that ensures that the percentage of rows containing valid currency codes does not exceed set thresholds.
- With DQOps, you can easily customize when the checks are run at the level of the entire connection, table, or individual check. Learn more about how to set schedules here.
- DQOps allows you to keep track of the issues that arise during data quality monitoring and send alert notifications directly to Slack. Learn more about incidents and Slack notifications.