Data quality is crucial for any business relying on data-driven decision-making. Poor quality data can lead to misguided strategies, wrong predictions, and, ultimately, a significant waste of resources. Fortunately, DBT (Data Build Tool) provides a robust mechanism to improve data quality in your data warehouse. This article will explore how you can leverage DBT to handle data quality issues effectively. DBT provides a robust toolkit for managing data quality issues, including powerful testing capabilities, detailed documentation, and the ability to create data quality dashboards.
Understand the Importance of Testing in DBT
Testing is one of the primary ways DBT helps maintain data quality. DBT’s built-in testing framework enables you to write tests that assert truths about your data, thereby identifying problems before they affect your reports or models.
Schema Tests
Schema tests are pre-packaged tests in DBT that verify simple assertions about your data. For instance, if you have a table freshers_in_customers and you want to ensure that the email field is unique and never null, you can define these tests in your schema.yml file like this:
version: 2
models:
- name: freshers_in_customers
columns:
- name: email
tests:
- unique
- not_nullWhen you run dbt test, DBT will check every record in the freshers_in_customers table to ensure that the email field is unique and not null. If any records fail these tests, DBT will report them, allowing you to quickly identify and resolve data quality issues.
Custom Tests
While schema tests can cover many common data quality checks, you might encounter scenarios where you need more specific tests. DBT allows you to create custom tests for such situations.
Suppose you want to verify that the joining_date for all entries in the freshers_in_employees table is not in the future. You can define a custom test in a separate .sql file as follows:
-- tests/freshers_in_future_joining_date.sql
{{
config(
severity='high'
)
}}
SELECT *
FROM {{ref('freshers_in_employees')}}
WHERE joining_date > CURRENT_DATE
This test will return all rows where joining_date is in the future. By setting the severity to ‘high’, you indicate that this test is crucial for your data quality standards.
Utilizing Sources and Documentation for Data Quality
Properly defined sources in DBT provide the first layer of control and awareness about your data quality. By defining sources, you create an explicit contract about what your raw data should look like, allowing you to catch discrepancies early in your pipeline.
Documentation is another powerful feature of DBT that helps maintain data quality. DBT allows you to document your models, columns, tests, and sources. Good documentation provides context about your data and transformations, making it easier to understand the expected state and behavior of your data.
Implementing Data Quality Dashboards
Beyond built-in and custom tests, you can use DBT to create data quality dashboards. These dashboards can show the status of your DBT tests, helping stakeholders stay informed about the state of your data quality. For instance, you can create a model that aggregates your test results:
-- models/freshers_in_data_quality_dashboard.sql
WITH latest_run AS (
SELECT
MAX(ended_at) AS ended_at
FROM {{ref('dbt_test_results')}}
)
SELECT
result,
COUNT(*) AS tests_count
FROM {{ref('dbt_test_results')}} AS results
JOIN latest_run
ON results.ended_at = latest_run.ended_at
GROUP BY result
This model gives you the count of passing and failing tests from the latest run, which you can then visualize in your favorite BI tool to create a data quality dashboard.
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