Databricks Data Engineer Associate Question 12
Select 3Which of the following improvements in data quality are achieved in a data lakehouse compared to a traditional data lake?
- A
Enforcement of schema and data validation during writes
- B
Support for ACID transactions to ensure data consistency
- C
Limited ability to handle real-time data processing
- D
Seamless integration of batch and streaming data pipelines
- E
Reduced dependency on metadata management tools for query optimization
Show answer and explanation
Correct answers: A, B, D
Explanation
Data lakehouses provide significant improvements in data quality over traditional data lakes by enforcing schema and data validation, supporting ACID transactions, and enabling seamless integration of batch and streaming pipelines. These features help address many of the challenges faced in maintaining data quality in traditional data lakes.
- A. Correct.
Schema enforcement and data validation are core features of the data lakehouse, ensuring data quality during writes. In traditional data lakes, this is often not enforced, leading to potential data inconsistencies.
- B. Correct.
Data lakehouses support ACID transactions, ensuring data consistency and reliability, a feature typically lacking in traditional data lakes.
- C. Incorrect.
This is incorrect because data lakehouses are capable of efficiently handling real-time data processing, unlike traditional data lakes where this capability is limited.
- D. Correct.
Data lakehouses are designed to seamlessly integrate batch and streaming pipelines, improving data quality by enabling unified processing and reducing latency issues.
- E. Incorrect.
While data lakehouses improve metadata handling, they do not eliminate the need for metadata tools entirely. Instead, they enhance query performance by integrating metadata more effectively.