You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks…

Dataform provides native assertion capabilities to validate data quality constraints such as uniqueness and null checks directly within the pipeline code.

Submitted by renata2k· Mar 30, 2026Ensuring solution quality

Question

You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

Options

ABuild BigQuery user-defined functions (UDFs).
BCreate Dataplex data quality tasks.
CBuild Dataform assertions into your code.
DWrite a Spark-based stored procedure.

Why each option

Dataform provides native assertion capabilities to validate data quality constraints such as uniqueness and null checks directly within the pipeline code.

ABuild BigQuery user-defined functions (UDFs).

BigQuery UDFs are for transforming or computing values, not for declaratively enforcing data quality constraints across a Dataform pipeline.

BCreate Dataplex data quality tasks.

Dataplex data quality tasks are a valid standalone quality solution but require separate configuration and are not natively integrated into Dataform pipeline execution.

CBuild Dataform assertions into your code.

DWrite a Spark-based stored procedure.

Spark-based stored procedures add unnecessary complexity and cost for simple null/uniqueness checks that Dataform handles natively.

Concept tested: Dataform assertions for data quality validation

Source: https://cloud.google.com/dataform/docs/assertions

Topics

#Dataform#BigQuery ELT#assertions#data quality checks

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