Here’s an example of how you can use dbt to automate data testing:
Let’s say you have a table in your data warehouse called orders, which contains information about orders placed by customers. You want to ensure that the data in this table is accurate and consistent over time, so you decide to create a dbt test that checks for the following:
- Every order has a unique order ID.
- Every order has a customer ID that matches a valid customer in the customers table
- Every order has a product ID that matches a valid product in the products table.
- The total cost of each order is equal to the sum of the costs of all products in the order.
To create this test in dbt, you would define a new test in your orders.sql model like this:
-- orders.sql
{{
config(
materialized='table'
)
}}
select *
from {{ ref('orders_base') }}
where not exists (
select 1 from customers where customers.customer_id = orders.customer_id
)
or not exists (
select 1 from products where products.product_id = orders.product_id
)
or total_cost <> (
select sum(cost) from products where products.product_id in (
select product_id from order_products where order_id = orders.order_id
)
)
In this test, we first select all the rows from the orders_base table, which contains the raw order data. We then use a series of not exists and sum subqueries to check for the four conditions we want to test.
Once you have defined this test, you can run it automatically as part of your dbt pipeline by including it in your dbt_project.yml file:
# dbt_project.yml
models:
orders:
tests:
- name: orders_data_validation
With this configuration, dbt will automatically run the orders_data_validation test every time you run the dbt test command, ensuring that your order data is accurate and consistent over time.
DBT provides a powerful framework for automated data testing, allowing you to define tests that validate the correctness of your data and automatically run those tests as part of your data pipeline. This can be critical for ensuring data accuracy and maintaining data quality over time.
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