This page describes the expect_column_values_to_match_regex
test in dbt, which ensures that column values adhere to a specified regular expression pattern. This test is crucial for validating the format of string data within a database to confirm it meets expected textual patterns.
How it Works
The expect_column_values_to_match_regex
test applies a regular expression (regex) to each value in a specific column to check if the values conform to the defined pattern. This functionality is essential for maintaining structured data that often requires specific formatting, such as phone numbers, email addresses, or custom identifiers.
Steps and Conditions:
- Column and Regex Selection: The column to be tested is defined, and a regex pattern is specified to determine the valid format for the values in this column.
- Regular Expression Match: The test evaluates each value in the column to see if it matches the regex pattern.
- Optional Configuration:
- Raw String: Set
is_raw
to true if the regex pattern needs to escape characters, marking it as a raw string. - Regex Flags: The
flags
parameter can include flags such as 'i' for case-insensitive matching, enhancing the flexibility of the match requirements. - Row Condition: The
row_condition
can specify conditions to filter which rows are included in the test.
- Raw String: Set
- Outcome:
- Pass: If all evaluated column values match the regex, the test passes, verifying the data conforms to the desired format.
- Fail: If any value does not match the regex, the test fails, indicating discrepancies that require attention.
Example Usage: Fintech
In a Fintech company, ensuring that transaction IDs are formatted correctly is crucial for data integrity and reliable transaction tracking. The expect_column_values_to_match_regex
test can be used to validate the format of the transaction ID in a transactions
table.
Consider a scenario where each transaction ID should follow a specific format, for example, two letters followed by six numbers (e.g., "AB123456").
This setup ensures that the transaction_id
column in the transactions
model of the Fintech platform contains values that all conform to the specified regex pattern. This is crucial for maintaining systematic transaction records and supporting efficient, accurate data retrieval and processing.