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Last updated: November 13, 2024

How to measure the percentage of duplicate values using a data quality check

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

Overview

This example verifies that the percentage of duplicate values in a column does not exceed the maximum accepted percentage.

PROBLEM

Austin-311-Public-Data provides the residents of Austin with a simple single point of contact for every city department.

What started as police non-emergency line for the City of Austin has become a robust Citywide Information Center where ambassadors are available to answer residents’ concerns 24 hours a day, 7 days a week, and 365 days a year.

The unique_key column contains unique key data. We want to verify the percent of duplicated values on unique_key column.

SOLUTION

We will verify the data of bigquery-public-data.austin_311.311_service_requests using monitoring duplicate_percent column check. Our goal is to verify if the percentage of duplicated values in unique_key column does not exceed set thresholds.

In this example, we will set the maximum percentage threshold level for the check:

  • error: 1.0%

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

VALUE

If the percentage of duplicated values on unqiue_key column exceed 1.0%, an error alert will be triggered.

Data structure

The following is a fragment of the bigquery-public-data.austin_311.311_service_requests dataset. Some columns were omitted for clarity.
The unique_key column of interest contains unique values.

unique_key complaint_description source source status_change_date created_date
19-00454912 Parking Machine Issue Phone Closed 12/3/2019 6:54:59 11/30/2019 11:33:22
20-00288726 Community Connections - Coronavirus Phone Closed 7/16/2020 11:26:40 7/16/2020 10:21:17
19-00458482 Parking Machine Issue Phone Closed 12/5/2019 6:41:42 12/3/2019 12:57:47
17-00207653 Street Light Issue- Address Web Closed 7/20/2017 12:33:20 7/20/2017 11:19:51
18-00118937 Parking Machine Issue Phone Closed 4/25/2018 13:30:43 4/24/2018 8:30:23
20-00525858 Community Connections - Coronavirus Phone Closed 12/29/2020 14:13:49 12/28/2020 17:26:17
14-00150037 Street Light Issue- Multiple poles/multiple streets Phone Closed 7/21/2014 14:52:20 7/21/2014 14:36:47
14-00181676 Parking Machine Issue Phone Closed 8/28/2014 10:40:32 8/27/2014 11:32:21

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 Data quality check editor 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.

Duplicate-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 0%, which is below the maximum threshold level set in the error (1.0%). The check gives a correct result (notice the green 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.

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 percentage threshold level for the check:

  • error: 1.0%

The highlighted fragments in the YAML file below represent the segment where the monitoring daily_duplicate_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:
    unique_key:
      type_snapshot:
        column_type: STRING
        nullable: true
      monitoring_checks:
        daily:
          uniqueness:
            daily_duplicate_percent:
              error:
                max_percent: 1.0
    source:
      type_snapshot:
        column_type: STRING
        nullable: true
    created_date:
      type_snapshot:
        column_type: TIMESTAMP
        nullable: true

In this example, we have demonstrated how to use DQOps to verify the uniqueness of data in a column. By using the duplicate_percent column check, we can monitor that the percentage of duplicate values in a column does not exceed the maximum accepted percentage. If it does, you will get an error result.

Next steps