Databricks Data Engineer Professional Question 165
Select 3You are designing a Delta Lake pipeline to process sensitive customer data. Ensuring data quality is critical, and you need to decide on the best approach to enforce data quality constraints during data ingestion. Which of the following combination of features in Delta Lake would be the most suitable for this use case?
- A
Define column-level constraints using Delta Lake's
constraintsfeature to enforce schema-level checks. - B
Use Delta Lake's
ZORDERoptimization to ensure data is written in a specific order for quality enforcement. - C
Leverage Delta Lake's
NOT NULLconstraint to ensure critical columns do not contain null values. - D
Implement Delta Lake's
CHECKconstraints to validate custom business rules for incoming data. - E
Rely on Delta Lake's
VACUUMoperation to remove invalid or low-quality data from the table.
Show answer and explanation
Correct answers: A, C, D
Explanation
To enforce data quality in Delta Lake during data ingestion, you should leverage features like constraints (for schema-level checks), NOT NULL (to prevent null values in critical columns), and CHECK constraints (for domain-specific rules). These features are purpose-built for enforcing data quality at the time of data writes. ZORDER and VACUUM are not relevant for this use case as they address performance optimization and storage cleanup, respectively.
- A. Correct.
Delta Lake's
constraintsfeature allows you to define column-level constraints, such asNOT NULLorCHECK, which are enforced during data writes. This is a strong approach for schema-level data quality enforcement. - B. Incorrect.
ZORDERis used for optimizing query performance by co-locating related data on storage. It does not directly enforce data quality. - C. Correct.
The
NOT NULLconstraint ensures that specified columns cannot contain null values, which is critical for maintaining data quality in specific use cases. - D. Correct.
CHECKconstraints allow you to validate custom business rules, such as ensuring column values fall within a specific range. This is a robust way to enforce domain-specific data quality rules. - E. Incorrect.
VACUUMis used for cleaning up stale data files, but it does not enforce data quality during data ingestion.