Number of invalid emails

Verifies that the number of invalid emails in a monitored column does not exceed the maximum accepted count.

PROBLEM

Here is a table with some sample customer data. In this example, we will monitor the email column.

The email column contains email values. We want to verify that the number of invalid emails does not exceed set thresholds.

SOLUTION

We will verify the data using monitoring string_invalid_email_count column check. Our goal is to verify if the number of invalid email values in email column does not exceed set thresholds.

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

warning: 0
error: 10
fatal: 15

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

VALUE

If the number of invalid email values exceed 0, a warning alert will be triggered.

Data structure

The following is a fragment of the DQOps 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

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

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

To execute the check 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 Monitoring Checks tab.

In this tab you can find a list of data quality checks.

Run the enabled check using the Run check button.

You can also run all checks for the check category using the Run check button located at the end of the row with the name of the check group.

Access the results by clicking the Results button.

Within the Results window, you will see three categories: Sensor readouts, Check results, and Execution errors. The Sensor readouts category displays the values obtained by the sensors from the data source. The Check results category shows the severity level that result from the verification of sensor readouts by set rule thresholds. The Execution errors category displays any error that occurred during the check's execution.

Review the results which should be similar to the one below.

The actual value in this example is 22, which is above the maximum threshold level set in the warning (0). The check gives a fatal error (notice the red square on the left of the name of the check).
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.
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 count summary dashboard showing results by results per check, number of issues per connection and number of issues per table.

Configuring a schedule at connection level

With DQOps, you can easily customize when checks are run by setting schedules. You can set schedules for an entire connection, table, or individual check.

After running the daily monitoring checks, let's set up a schedule for the entire connection to execute the checks every day at 12:00.

Navigate to the Data Source section.
Choose the connection from the tree view on the left.
Click on the Schedule tab.
Select the Monitoring Daily tab
Select the Run every day at option and specify the time as 12:00.
Once you have set the schedule, click on the Save button to save your changes.
Enable the scheduler by clicking the toggle button.

Once a schedule is set up for a particular connection, it will execute all the checks that have been configured across all tables associated with that connection.

You might also want to check the Running checks with a scheduler example.

YAML configuration file

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

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

warning: 0
error: 10
fatal: 15

The highlighted fragments in the YAML file below represent the segment where the monitoring daly_string_invalid_email_count 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:
    id:
      type_snapshot:
        column_type: INT64
        nullable: true
    email:
      type_snapshot:
        column_type: STRING
        nullable: true
      monitoring_checks:
        daily:
          strings:
            daily_string_invalid_email_count:
              warning:
                max_count: 0
              error:
                max_count: 10
              fatal:
                max_count: 15
    email_ok:
      type_snapshot:
        column_type: INT64
        nullable: true
    surrounded_by_whitespace:
      type_snapshot:
        column_type: STRING
        nullable: true

Running the checks in the example and evaluating the results using DQOps 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 DQOps Shell:

check run

Review the results which should be similar to the one below. The number of the invalid email values in the email column above 15 and the check raised the fatal error.

+-------------------+-----------------------------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection         |Table                                                |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+-------------------+-----------------------------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|invalid_email_count|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 invalid_email_count (bigquery)
SQL to be executed on the connection:
SELECT
    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 0
            ELSE 1
        END
    ) AS actual_value,
    DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH) AS time_period,
    TIMESTAMP(DATE_TRUNC(CAST(CURRENT_TIMESTAMP() AS DATE), MONTH)) 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 invalid emails in this example is 22, which is above the maximum threshold level set in the fatal error (15).

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

Results returned by the sensor:
+------------+-----------+--------------------+
|actual_value|time_period|time_period_utc     |
+------------+-----------+--------------------+
|22          |2023-08-01 |2023-08-01T00:00:00Z|
+------------+-----------+--------------------+
**************************************************

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 string_invalid_email_count check used in this example, go to the check details section.
You might be interested in another validity check that evaluates that the number of invalid IP4 address in a column does not exceed the maximum accepted count.
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.