Databricks Data Engineer Associate Question 13
Select 3A company previously used a data lake to store raw data but faced challenges with inconsistent schemas, poor data governance, and difficulty ensuring data accuracy. They are now transitioning to a data lakehouse. Which aspects of the data lakehouse architecture help improve data quality compared to the data lake?
- A
Support for ACID transactions to ensure consistency during data modifications
- B
Schema enforcement and evolution to ensure data adheres to predefined structures
- C
Decoupling of storage and compute to allow independent scaling
- D
Integration with machine learning frameworks for advanced analytics
- E
Built-in governance features like versioning and access control
Show answer and explanation
Correct answers: A, B, E
Explanation
The data lakehouse improves data quality over the traditional data lake by introducing ACID transactions, schema enforcement and evolution, and built-in governance features. These capabilities address common data quality challenges in data lakes, such as inconsistent schemas, lack of transactional support, and poor governance.
- A. Correct.
ACID transactions are a key feature of the data lakehouse that ensures data consistency and reliability when data is updated, improving data quality.
- B. Correct.
Schema enforcement and evolution ensure that data adheres to predefined structures, reducing issues caused by inconsistent schemas.
- C. Incorrect.
While decoupling storage and compute is an important feature of modern architectures, it does not directly address data quality improvements.
- D. Incorrect.
Integration with machine learning frameworks is a feature of the lakehouse, but it does not directly improve data quality.
- E. Correct.
Built-in governance features like versioning and access control help maintain data integrity and ensure proper management of data quality.