Databricks Data Engineer Associate Question 6
Select 3A data engineering team is evaluating the differences between a data lakehouse and a traditional data warehouse. Which of the following statements accurately describe the relationship between the two?
- A
A data lakehouse combines the capabilities of a data lake and a data warehouse into a single platform.
- B
A data warehouse is designed for storing structured data, while a data lakehouse can handle both structured and unstructured data.
- C
A data lakehouse eliminates the need for ETL processes when integrating with a data warehouse.
- D
A data warehouse typically uses a schema-on-write model, while a data lakehouse supports both schema-on-read and schema-on-write.
- E
A data lakehouse cannot perform the same analytical workloads as a data warehouse.
Show answer and explanation
Correct answers: A, B, D
Explanation
A data lakehouse bridges the gap between traditional data lakes and data warehouses by combining their strengths. It allows for the storage of both structured and unstructured data, supports schema-on-read and schema-on-write, and can perform similar analytical workloads as a data warehouse. However, ETL processes may still be required depending on the specific use case.
- A. Correct.
Correct: A data lakehouse merges the best features of a data lake and a data warehouse, offering unified capabilities in a single architecture.
- B. Correct.
Correct: Traditional data warehouses primarily handle structured data, while a data lakehouse is designed to process both structured and unstructured data.
- C. Incorrect.
Incorrect: While a data lakehouse simplifies data workflows, it does not eliminate the need for ETL processes in all scenarios, such as when transforming data for specific analytical use cases.
- D. Correct.
Correct: Data warehouses rely on schema-on-write for data modeling, whereas a data lakehouse can support both schema-on-write (for structured data) and schema-on-read (for unstructured or semi-structured data).
- E. Incorrect.
Incorrect: A data lakehouse is designed to perform the same analytical workloads as a data warehouse, with additional capabilities for handling unstructured and semi-structured data.