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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.

To navigate to a list of checks prepared in the example using the user interface:

Navigating to a list of checks

  1. 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.

  2. 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.

  3. 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.

Run check

View detailed check results

Access the detailed results by clicking the Results button. The results should be similar to the one below.

valid_uuid_format_percent check results

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