Percentage of valid emails

Verifies that the percentage of S in a column does not exceed the maximum accepted percentage.

PROBLEM

Here is a table with some sample customer data. In this example, we will monitor the email column and verify that each email is in the correct format.

The email column contains email values. We want to verify the percent of invalid email values on email column.

SOLUTION

We will verify the data using profiling valid_email_percent column check. Our goal is to verify if the percentage of valid email values in email column does not fall below set thresholds.

In this example, we will set three minimum percentage thresholds levels for the check:

• warning: 99.0%
• error: 98.0%
• fatal: 95.0%

If you want to learn more about checks and threshold levels, please refer to the DQO concept section.

VALUE

If the percentage of valid email values falls below 99.0%, a warning alert will be triggered.

Data structure

The following is a fragment of the DQO dataset. Some columns were omitted for clarity.
The email column of interest contains both valid and invalid email values.

id	email	email_ok	surrounded_by_whitespace	surrounded_by_whitespace_ok	null_placeholder
24	sam.black@coca-cola.com	0	Iowa	1	n/d
20	jon.doe@mail.com	0	Hawaii	0	married
29	user9@mail.com	0	Texas	1	married
5	!@user5@mail.com	1	Philade lphia	1	married
27	_example@mail.com	0	Louisiana	1
7	^&*user7@mail.com	1	Delaware	1	empty
15	user7@mail	1	Connecticu	1	missing

YAML configuration file

The YAML configuration file stores both the table details and checks configurations.

In this example, we have set three minimum percentage thresholds levels for the check:

• warning: 99.0%
• error: 98.0%
• fatal: 95.0%

The highlighted fragments in the YAML file below represent the segment where the profiling valid_email_percent check is configured.

If you want to learn more about checks and threshold levels, please refer to the DQO concept section.

apiVersion: dqo/v1
kind: table
spec:
  incremental_time_window:
    daily_partitioning_recent_days: 7
    monthly_partitioning_recent_months: 1
  columns:
    id:
      type_snapshot:
        column_type: INT64
        nullable: true
    email:
      type_snapshot:
        column_type: STRING
        nullable: true
      profiling_checks:
        pii:
          profile_valid_email_percent:
            comments:
            - date: 2023-05-04T11:13:34.182+00:00
              comment_by: user
              comment: "In this example, values in \"email\" column are verified whether\
                \ the percentage of invalid email values does not exceed the indicated\
                \ thresholds."
            parameters: {}
            warning:
              min_percent: 99.0
            error:
              min_percent: 98.0
            fatal:
              min_percent: 95.0
    email_ok:
      type_snapshot:
        column_type: INT64
        nullable: true
    surrounded_by_whitespace:
      type_snapshot:
        column_type: STRING
        nullable: true
    surrounded_by_whitespace_ok:
      type_snapshot:
        column_type: INT64
        nullable: true
    null_placeholder:
      type_snapshot:
        column_type: STRING
        nullable: true

Running the checks in the example and evaluating the results using the graphical interface

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example using the graphical interface:

1. Go to Profiling section.

2. Select the table or column mentioned in the example description from the tree view on the left.

3. Select Advanced Profiling tab.

4. Run the enabled check using the Run check button.

5. Review the results by opening the Check details button.

6. You should see the results as the one below. The actual value in this example is 40, which is below the minimum threshold level set in the warning (99.0%). The check gives a fatal error (notice the red square on the left of the name of the check).

1. After executing the checks, synchronize the results with your DQO cloud account sing the Synchronize button located in the upper right corner of the graphical interface.

2. To review the results on the data quality dashboards go to the Data Quality Dashboards section and select the dashboard from the tree view on the left. Below you can see the results displayed on the Issues per check dashboard showing results by check category, check, failed tests and one day details.

Running the checks in the example and evaluating the results using DQO Shell

The detailed explanation of how to run the example is described here.

To execute the check prepared in the example, run the following command in DQO Shell:

check run

You should see the results as the one below. The percent of the invalid email values in the email column is below 95.0% and the check raised the fatal error.

Check evaluation summary per table:
+--------------------+-----------------------------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection          |Table                                                |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+--------------------+-----------------------------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|valid_email_percent_|dqo_ai_test_data.string_test_data_3888926926528139965|1     |1             |0            |0       |0     |1           |0               |
+--------------------+-----------------------------------------------------+------+--------------+-------------+--------+------+------------+----------------+

For a more detailed insight of how the check is run, you can initiate the check in debug mode by executing the following command:

check run --mode=debug

In the debug mode you can view the SQL query (sensor) executed in the check.

**************************************************
Executing SQL on connection valid_email_percent_ (bigquery)
SQL to be executed on the connection:
SELECT
    CASE
        WHEN COUNT(*) = 0 THEN 100.0
        ELSE 100.0 * SUM(
            CASE
                WHEN REGEXP_CONTAINS(CAST(analyzed_table.`email` AS STRING), r"^[A-Za-z_]+[A-Za-z0-9._]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,4}$")
                    THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CURRENT_TIMESTAMP() AS time_period,
    TIMESTAMP(CURRENT_TIMESTAMP()) AS time_period_utc
FROM `dqo-ai-testing`.`dqo_ai_test_data`.`string_test_data_3888926926528139965` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
**************************************************

You can also see the results returned by the sensor. The actual value of valid email values in this example is 40.0%, which is below the minimum threshold level set in the fatal error (95.0%).

**************************************************
Finished executing a sensor for a check valid_email_percent on the table dqo_ai_test_data.string_test_data_3888926926528139965 using a sensor definition column/pii/valid_email_percent, sensor result count: 1

Results returned by the sensor:
+------------+------------------------+------------------------+
|actual_value|time_period             |time_period_utc         |
+------------+------------------------+------------------------+
|40.0        |2023-05-04T11:14:02.697Z|2023-05-04T11:14:02.697Z|
+------------+------------------------+------------------------+
**************************************************