Using Dataform for ELT in BigQuery, you must perform uniqueness and null checks on final tables. How do you integrate these checks efficiently into the pipeline?

Dataform assertions are the most efficient and integrated way to perform uniqueness and null checks on final tables within a Dataform ELT pipeline. Assertions are defined directly within your Dataform SQLX files, allowing them to run automatically as part of your data pipeline execution. This ensures data quality checks are co-located with your table definitions and transformations, making them easy to manage, version control, and debug. Implementing BigQuery UDFs would require separate execution steps and management outside the core Dataform build process. Dataplex data quality tasks are a good option for broader data quality monitoring but are less integrated for in-pipeline ELT checks compared to Dataform's native assertions. Writing a Spark-based stored procedure is overly complex and introduces an external processing engine for a task Dataform handles natively within BigQuery.