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Percentage of valid USA zipcodes

Verifies that the percentage of valid USA zip code in a column does not fall below the minimum 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 incident_zip column contains USA zipcode data. We want to verify the percent of valid USA zipcode on incident_zip column.

SOLUTION

We will verify the data of bigquery-public-data.austin_311.311_service_requests using profiling valid_usa_zipcode_percent column check. Our goal is to verify if the percentage of valid USA zipcode values in the incident_zip column does not fall below the setup 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 zipcode values falls below 99.0%, a warning 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 incident_zip column of interest contains valid USA zipcode values.

city incident_zip country state_plane_x_coordinate state_plane_y_coordinate latitude
78613
78664
78729
austin 78746 0.0 0 3.442386682
78701
78739
78751
78733
78786

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_usa_zipcode_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:
    incident_zip:
      type_snapshot:
        column_type: INT64
        nullable: true
      profiling_checks:
        pii:
          profile_valid_usa_zipcode_percent:
            comments:
              - date: 2023-04-14T09:06:29.764+00:00
                comment_by: user
                comment: In this example, values in "incident_zip" column are verified
                  whether the percentage of USA zip-code values reaches the indicated
                  thresholds.
            warning:
              min_percent: 99.0
            error:
              min_percent: 98.0
            fatal:
              min_percent: 95.0
    county:
      type_snapshot:
        column_type: STRING
        nullable: true
    state_plane_x_coordinate:
      type_snapshot:
        column_type: STRING
        nullable: true
    state_plane_y_coordinate:
      type_snapshot:
        column_type: FLOAT64
        nullable: true
    latitude:
      type_snapshot:
        column_type: FLOAT64
        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:

Navigating to a list of checks

  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. Run check

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

  6. You should see the results as the one below. The actual value in this example is 98, which is below the minimum threshold level set in the warning (99.0%). The check gives a warning (notice the yellow square on the left of the name of the check).

Valid-usa-zipcode-percent check results

  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 Daily tests per column dashboard showing results by connections, schemas, tables, data groups and checks executed per column and day of month.

Valid-usa-zipcode-percent check results on Daily tests per column dashboard

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 valid USA zipcode values in the incident_zip column is below 99.0% and the check raised a warning. 
Check evaluation summary per table:
+----------+-------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection|Table                          |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+----------+-------------------------------+------+--------------+-------------+--------+------+------------+----------------+
|austin_311|austin_311.311_service_requests|1     |1             |1            |1       |0     |0           |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 austin_311 (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.`incident_zip` AS STRING),
                    r"^[0-9]{5}(?:-[0-9]{4})?$"
                ) THEN 1
                ELSE 0
            END
        ) / COUNT(*)
    END AS actual_value,
    CURRENT_TIMESTAMP() AS time_period,
    TIMESTAMP(CURRENT_TIMESTAMP()) AS time_period_utc
FROM `bigquery-public-data`.`austin_311`.`311_service_requests` 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 in this example is 98.89%, which is below the minimal threshold level set in the warning (99.0%).

**************************************************
Finished executing a sensor for a check valid_usa_zipcode_percent on the table austin_311.311_service_requests using a sensor definition column/pii/valid_usa_zipcode_percent, sensor result count: 1

Results returned by the sensor:
+----------------+------------------------+------------------------+
|actual_value    |time_period             |time_period_utc         |
+----------------+------------------------+------------------------+
|98.8902793912375|2023-04-25T13:28:04.147Z|2023-04-25T13:28:04.147Z|
+----------------+------------------------+------------------------+
**************************************************