Percentage of integer values in range
Verifies that the percentage of integer values from a range in a column does not exceed the minimum accepted percentage.
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 percent of values between 0 ad 100,000 in values column.
SOLUTION
We will verify the data of bigquery-public-data.america_health_rankings.ahr using profiling
values_in_range_numeric_percent column check.
Our goal is to verify if the percentage of values in a range in the values column does not fall below the set thresholds.
In this example, we will set three minimum percentage thresholds levels for the check:
- warning: 99.0%
- error: 95.0%
- fatal: 90.0%
If you want to learn more about checks and threshold levels, please refer to the DQO concept section.
VALUE
If the percentage of valid values falls below 5.0%, 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 value column of interest contains values in range between 0 and 100,000.
| edition | report_type | measure_name | state_name | subpopulation | value |
|---|---|---|---|---|---|
| 2021 | 2021 Health Disparities | Able-Bodied | California | 87 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Colorado | 87 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Hawaii | 87 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Kentucky | 79 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Maryland | 87 | |
| 2021 | 2021 Health Disparities | Able-Bodied | New Jersey | 87 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Utah | 88 | |
| 2021 | 2021 Health Disparities | Able-Bodied | West Virginia | 77 | |
| 2021 | 2021 Health Disparities | Able-Bodied | Arkansas | Female | 78 |
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: 95.0%
- fatal: 90.0%
The highlighted fragments in the YAML file below represent the segment where the profiling values_in_range_numeric_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:
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
profiling_checks:
numeric:
profile_values_in_range_numeric_percent:
comments:
- date: 2023-05-09T07:28:29.188+00:00
comment_by: user
comment: "In this example, the values in the `values` column are verified\
\ that they are within the set range and that the percentage of these\
\ values does not exceed the set thresholds."
parameters:
min_value: 0.0
max_value: 100000.0
warning:
min_percent: 99.0
error:
min_percent: 95.0
fatal:
min_percent: 90.0
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
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:
-
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 in this example is 92, which is below the minimum threshold level set in the warning (99.0%). The check gives a warning (notice the orange square on the left of the name of the check).
-
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 DQ KPIs per check type dashboard showing results by KPI, KPI per check type, profiling KPI, recurring KPI and partitioned KPI.
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:
You should see the results as the one below. The percentage of values between 1 and 100,000 in thevalue column is less than 95% and more than 90% and the check raised an error.
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 |0 |0 |1 |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:
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
100.0 * SUM(
CASE
WHEN analyzed_table.`value` >= 0.0 AND analyzed_table.`value` <= 100000.0 THEN 1
ELSE 0
END
) / COUNT(*) 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 92.9%, which is below the minimal threshold level set in the warning alert(95.0%).
**************************************************
Finished executing a sensor for a check values_in_range_numeric_percent on the table america_health_rankings.ahr
using a sensor definition column/numeric/values_in_range_numeric_percent, sensor result count: 1
Results returned by the sensor:
+-----------------+------------------------+------------------------+
|actual_value |time_period |time_period_utc |
+-----------------+------------------------+------------------------+
|92.87799504268797|2023-05-09T07:20:03.160Z|2023-05-09T07:20:03.160Z|
+-----------------+------------------------+------------------------+
**************************************************