dbt test: expect_table_column_count_to_equal_other_table

This page discusses the expect_table_column_count_to_equal_other_table test from the dbt-expectations package. This test ensures that two data models (tables, seeds, or other sources) have the same number of columns, which is critical when expecting structural consistency for data integrity checks or during data migrations.

How it Works

The expect_table_column_count_to_equal_other_table test is specifically designed to validate that the column count of one model matches that of another specified model. Ensuring that two data structures are aligned in terms of column count is crucial for tasks such as data migrations or when integrating systems that require identical table structures.

Steps and Conditions:

1. Model Selection: Choose the two models to compare. This test will evaluate the number of columns in both.
2. Configuration: Specify the model to compare to using the compare_model parameter.
3. Execution: The test counts the number of columns in both specified models.
4. Outcome:
   • Pass: If the number of columns in both models is the same, the test passes, indicating that the models are structurally consistent with each other.
   • Fail: If the column counts differ, the test fails, signaling a discrepancy between the two models that may need to be addressed to ensure consistent data handling or migration.

Example Usage: E-commerce

For an E-commerce platform, it is essential to maintain consistent database schema across different environments (e.g., development, staging, production) to prevent issues in data processing and reporting.

Imagine having two models: current_products in the production environment and staging_products in the staging environment. Both tables should have identical structures to correctly test new features before going live.

models:
  - name: current_products
    tests:
      - dbt_expectations.expect_table_column_count_to_equal_other_table:
          compare_model: ref("staging_products")

In this example, the expect_table_column_count_to_equal_other_table test ensures that any changes made in the staging environment that affect the structure of the staging_products table are replicated in the current_products table. This alignment is critical to avoiding discrepancies that might affect data-driven decisions or operations in the E-commerce platform.