Databricks Data Engineer Professional Question 166
Select 3You are designing a Delta Lake pipeline to process customer transaction data. To ensure data quality, you want to enforce constraints to prevent invalid or corrupt data from being written to your Delta tables. Which of the following approaches should you use to achieve this, considering the strengths and limitations of Delta Lake features?
- A
Use Delta Lake's constraints like NOT NULL and CHECK constraints to enforce data validity at the table level.
- B
Use Delta Lake's schema enforcement to reject writes that do not match the defined table schema.
- C
Rely solely on partition pruning to improve query performance and ensure data quality.
- D
Implement custom validation logic using Apache Spark before data is written to the Delta table.
- E
Use Delta Lake's time travel feature to fix corrupted data after it has been written.
Show answer and explanation
Correct answers: A, B, D
Explanation
To enforce data quality in Delta Lake, you should combine constraints (e.g., NOT NULL, CHECK constraints) and schema enforcement with custom validation logic for advanced use cases. Constraints and schema enforcement act as the first line of defense, while custom validation logic helps handle complex rules. Partition pruning is unrelated to data quality, and time travel is for auditing or recovery rather than prevention.
- A. Correct.
Delta Lake's constraints like NOT NULL and CHECK constraints are effective for enforcing row-level data validity directly at the table level, ensuring compliance with specified rules.
- B. Correct.
Schema enforcement ensures that incoming data adheres to the defined schema, which helps prevent schema mismatch or incompatible data from being written to the table.
- C. Incorrect.
Partition pruning is a performance optimization technique and does not actively enforce data quality; it is unrelated to ensuring valid or clean data.
- D. Correct.
Custom validation logic in Apache Spark allows you to implement more complex business rules or data quality checks that cannot be enforced at the table schema level.
- E. Incorrect.
Delta Lake's time travel feature is useful for auditing or recovering historical data but does not prevent invalid or corrupt data from being written in the first place.