dbt test: expect_table_column_count_to_equal

This page details the expect_table_column_count_to_equal test from the dbt-expectations package, which verifies that the number of columns in a given model, seed, or source exactly matches a specified count. This test is essential for confirming the structure of your data tables, ensuring that they contain the correct number of columns as expected by your data contracts or schema definitions.

How it Works

The expect_table_column_count_to_equal test validates that the total number of columns present in a table matches an expected figure. Such validation is particularly crucial after migrations or when integrating new data sources to ensure that the table structure adheres strictly to specified schema requirements.

Steps and Conditions:

1. Model, Seed, or Source Selection: Identify the table or data model on which you wish to run the test.
2. Specify Expected Column Count: Declare the exact number of columns expected in the table using the value argument.
3. Execution: The test counts the columns in the specified table and compares the result with the expected number.
4. Outcome:
   • Pass: If the actual number of columns equals the expected value, the test passes, confirming that the table structure is as expected.
   • Fail: If the count of columns differs from the expected value, the test fails, indicating a discrepancy that needs resolution or further investigation.

Example Usage: Fintech Company

For a Fintech company, precise data table structures are vital due to the need for accurate financial reporting and compliance with regulatory requirements. Consider a scenario where a transactions table is expected to have a fixed set of columns to comply with financial auditing standards.

models:
  - name: transactions
    tests:
      - dbt_expectations.expect_table_column_count_to_equal:
          value: 8

In this example, the expect_table_column_count_to_equal test checks that the transactions table contains exactly 8 columns. This setup is critical to ensure that all necessary data fields are present for each transaction recorded, supporting accurate reporting and compliance in a Fintech context.