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Last updated: October 22, 2024

How to detect invalid emails using a data quality check

This sample shows how to use data quality checks in the DQOps platform to detect invalid emails and view the results on data quality dashboards.

Overview

This example 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 invalid_email_format_found 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 the maximum threshold level for the check:

  • warning: 0

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

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.

String-invalid-email-count 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 6, which is above the maximum threshold level set in the warning (0). Examples of bad emails can be viewed in the Error sampling tab.

String-invalid-email-error sampling

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.

Change a schedule at the 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 importing new tables, DQOps sets the schedule for 12:00 P.M. (noon) every day. Follow the steps below to change the schedule.

Change a schedule at the connection level

  1. Navigate to the Data Source section.

  2. Choose the connection from the tree view on the left.

  3. Click on the Schedule tab.

  4. Select the Monitoring daily tab

  5. Select the Run every day at and change the time, for example, to 10:00. You can also select any other option.

  6. Once you have set the schedule, click on the Save button to save your changes.

    By default, scheduler is active. You can turn it off by clicking on notification icon in the top right corner of the screen, and clicking the toggle button.

    Turn off scheduler

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 can read more about scheduling here.

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 the maximum threshold level for the check:

  • warning: 0

The highlighted fragments in the YAML file below represent the segment where the monitoring daily_invalid_email_format_found 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:
               patterns:
                  daily_invalid_email_format_found:
                     warning:
                        max_count: 0
      email_ok:
         type_snapshot:
            column_type: INT64
            nullable: true
      surrounded_by_whitespace:
         type_snapshot:
            column_type: STRING
            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_email_format_found column check, we can monitor that the number of invalid emails in a monitored column does not exceed the maximum accepted count. If it does, you will get a warning.

Next steps