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A data engineering team is developing a Delta Live Tables (DLT) pipeline comprising multiple tables that require identical data quality checks. To maximize maintainability and reduce redundancy, they need to share these expectations across the entire pipeline.
What is the most efficient and recommended approach to achieve this reuse?
A data engineering team is developing a Delta Live Tables (DLT) pipeline comprising multiple tables that require identical data quality checks. To maximize maintainability and reduce redundancy, they need to share these expectations across the entire pipeline.
What is the most efficient and recommended approach to achieve this reuse?
Real Exam
Last updated: January 6, 2026 at 15:40A
Maintain the data quality rules in a Delta table outside the pipeline's target schema and pass the schema name as a pipeline parameter to be queried at runtime.
B
Define the data quality rules in a separate Databricks notebook or Python file and import them as a module/library into each DLT notebook in the pipeline.
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