Percentage of strings matching date regex
Verifies that the percentage of strings matching the date format regex in a column does not exceed the maximum 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.
The source_date column contains non-standard date format. We want to verify the percent of values matches the indicated by the user date format on source_date column.
SOLUTION
We will verify the data of bigquery-public-data.america_health_rankings.ahr using profiling
string_match_date_regex_percent column check.
Our goal is to verify if the percentage of values matches the indicated by the user date format on source_date 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 data falls below 99.0%, 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 source_date column of interest contains non-standard date format, in this case this is YYYY-MM-DD.
| value | lower_ci | upper_ci | source | source_date |
|---|---|---|---|---|
| 87 | 87 | 87 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 87 | 87 | 87 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 87 | 86 | 87 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 79 | 79 | 79 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 87 | 86 | 87 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 87 | 87 | 88 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 88 | 88 | 88 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 77 | 76 | 77 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
| 78 | 78 | 79 | U.S. Census Bureau, American Community Survey PUMS | 2015-2019 |
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 string_match_date_regex_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:
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
source_date:
type_snapshot:
column_type: STRING
nullable: true
profiling_checks:
strings:
profile_string_match_date_regex_percent:
comments:
- date: 2023-04-26T11:00:58.444+00:00
comment_by: user
comment: "Values in range integers percent-to check - In this example,\
\ values in \"source_date\" column are verified whether the percentage\
\ of values in the indicated format (YYYY-MM-DD) reaches the indicated\
\ thresholds."
parameters:
date_formats: YYYY-MM-DD
warning:
min_percent: 99.0
error:
min_percent: 98.0
fatal:
min_percent: 95.0
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 0, which is below the minimum threshold level set in the warning (99.0%). The check gives a fatal error (notice the red 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 Affected tables per KPI dashboard showing results by issues per connection, issues per schema, issues per data quality dimension and issues per check category.
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 percent of valid date formats in thesource_date column is below the 95% and the check raised the Fatal 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 |0 |1 |0 |
+-----------------------+---------------------------+------+--------------+-------------+--------+------+------------+----------------+
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
CASE
WHEN COUNT(analyzed_table.`source_date`) = 0 THEN NULL
ELSE 100.0 * SUM(
CASE
WHEN SAFE.PARSE_DATE('%Y-%m-%d', analyzed_table.`source_date`) IS NOT NULL
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`.`america_health_rankings`.`ahr` AS analyzed_table
GROUP BY time_period, time_period_utc
ORDER BY time_period, time_period_utc
**************************************************
**************************************************
Finished executing a sensor for a check string_match_date_regex_percent on the table america_health_rankings.ahr using a sensor definition column/strings/string_match_date_regex_percent, sensor result count: 1
Results returned by the sensor:
+------------+------------------------+------------------------+
|actual_value|time_period |time_period_utc |
+------------+------------------------+------------------------+
|0.0 |2023-04-26T11:01:24.538Z|2023-04-26T11:01:24.538Z|
+------------+------------------------+------------------------+
**************************************************