Number of null values

Verifies that the number of null values in a column does not exceed the maximum accepted count.

PROBLEM

America’s Health Rankings provides an analysis of national health on a state-by-state basis by evaluating a historical and comprehensive set of health, environmental and socioeconomic data to determine national health benchmarks and state rankings.

The platform analyzes more than 340 measures of behaviors, social and economic factors, physical environment and clinical care data. Data is based on public-use data sets, such as the U.S. Census and the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS), the world’s largest, annual population-based telephone survey of over 400,000 people.

We want to verify the number of null values on source column.

SOLUTION

We will verify the data of bigquery-public-data.america_health_rankings.ahr using profiling nulls_count column check. Our goal is to verify that the number of null values in the source column does not exceed the set thresholds.

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

warning: 5
error: 10
fatal: 15

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

VALUE

If the number of not nulls values exceed 5, a warning alert will be triggered.

Data structure

The following is a fragment of the bigquery-public-data.america_health_rankings.ahr dataset. Some columns were omitted for clarity.
The source column of interest contains NULL values.

report_type	measure_name	state_name	subpopulation	source
2021 Health Disparities	Maternal Mortality	United States	Non-Metropolitan Area
2021 Health Disparities	Dedicated Health Care Provider	Indiana	Other Race	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Hawaii	Black/African American	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Kansas	Other Race	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Idaho		CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	New York	American Indian/Alaska Native	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Indiana	Black/African American	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Montana	High School Grad	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Alabama	Male	CDC, Behavioral Risk Factor Surveillance System
2021 Health Disparities	Dedicated Health Care Provider	Alaska	Male	CDC, Behavioral Risk Factor Surveillance System

YAML configuration file

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

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

warning: 5
error: 10
fatal: 15

The highlighted fragments in the YAML file below represent the segment where the profiling nulls_count 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:
    edition:
      type_snapshot:
        column_type: INT64
        nullable: true
    report_type:
      type_snapshot:
        column_type: STRING
        nullable: true
    measure_name:
      type_snapshot:
        column_type: STRING
        nullable: true
    state_name:
      type_snapshot:
        column_type: STRING
        nullable: true
    subpopulation:
      type_snapshot:
        column_type: STRING
        nullable: true
    value:
      type_snapshot:
        column_type: FLOAT64
        nullable: true
    lower_ci:
      type_snapshot:
        column_type: FLOAT64
        nullable: true
    upper_ci:
      type_snapshot:
        column_type: FLOAT64
        nullable: true
    source:
      type_snapshot:
        column_type: STRING
        nullable: true
      profiling_checks:
        nulls:
          profile_nulls_count:
            comments:
            - date: 2023-05-08T12:08:21.558+00:00
              comment_by: user
              comment: "In this exmple, values in the `source ` column are verified\
                \ whether the number of null values does not exceed the set thresholds."
            warning:
              max_count: 5
            error:
              max_count: 10
            fatal:
              max_count: 15
    source_date:
      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:

Navigating to a list of checks

Go to Profiling section.
Select the table or column mentioned in the example description from the tree view on the left.
Select Advanced Profiling tab.
Run the enabled check using the Run check button.
Review the results by opening the Check details button.
You should see the results as the one below. The actual value of null values in this example is 8, which is above the maximum threshold level set in the warning (5). The check gives a warning result (notice the yellow square on the left of the name of the check).

Null-count check results

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.
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 Affected tables dashboard showing results by issues per connection, issues per schema, issues per check category and severity level.

Null-count check results on Affected tables 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 number of null values in the source column is above 5 and the check raised warning.

Check evaluation summary per table:
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|Connection             |Table                      |Checks|Sensor results|Valid results|Warnings|Errors|Fatal errors|Execution errors|
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
|america_health_rankings|america_health_rankings.ahr|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 america_health_rankings (bigquery)
SQL to be executed on the connection:
SELECT
    SUM(
        CASE
            WHEN analyzed_table.`source` IS NULL THEN 1
            ELSE 0
        END
    ) AS actual_value,
    CURRENT_TIMESTAMP() AS time_period,
    TIMESTAMP(CURRENT_TIMESTAMP()) AS time_period_utc
FROM `bigquery-public-data`.`america_health_rankings`.`ahr` 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 8, which is above the maximum threshold level set in the warning (5).

**************************************************
Finished executing a sensor for a check nulls_count on the table america_health_rankings.ahr using a sensor definition column/nulls/null_count, sensor result count: 1

Results returned by the sensor:
+------------+------------------------+------------------------+
|actual_value|time_period             |time_period_utc         |
+------------+------------------------+------------------------+
|8           |2023-05-08T12:05:28.996Z|2023-05-08T12:05:28.996Z|
+------------+------------------------+------------------------+
**************************************************