Databricks Data Engineer Associate Question 7
Select 3A data engineering team is considering whether to implement a data lakehouse architecture or a traditional data warehouse for their analytics workflows. Which of the following statements correctly describe the relationship between a data lakehouse and a data warehouse?
- A
A data lakehouse combines the low-cost storage benefits of a data lake with the ACID transaction support of a data warehouse.
- B
A data lakehouse eliminates the need for ETL processes by directly supporting structured, semi-structured, and unstructured data.
- C
A data warehouse is optimized for real-time streaming data ingestion, which is not a primary focus of a data lakehouse.
- D
A data lakehouse enables both BI-style analytics and data science workloads in a single platform, unlike traditional data warehouses.
- E
A data lakehouse stores data in proprietary formats to optimize performance, whereas a data warehouse uses open formats for flexibility.
Show answer and explanation
Correct answers: A, B, D
Explanation
A data lakehouse is a modern data architecture that unifies the capabilities of data lakes and data warehouses. It provides low-cost storage, supports diverse data types, and enables transactional consistency, all while supporting both analytical and machine learning workloads. This makes it a more versatile option compared to traditional data warehouses, which are optimized primarily for structured data and BI workloads.
- A. Correct.
Correct: A data lakehouse combines the scalability and cost efficiency of a data lake with features such as ACID transactions and schema enforcement, which are traditionally associated with data warehouses.
- B. Correct.
Correct: A data lakehouse reduces the need for complex ETL processes by natively supporting multiple data formats and types, enabling direct query and processing.
- C. Incorrect.
Incorrect: Data warehouses are not typically optimized for real-time streaming data ingestion. While some modern warehouses may support streaming, it is not their primary design focus. Data lakehouses, on the other hand, can support streaming and batch data.
- D. Correct.
Correct: A key advantage of the data lakehouse is its ability to support both traditional BI workloads and data science/machine learning workflows, which are typically siloed in data warehouse and data lake architectures respectively.
- E. Incorrect.
Incorrect: Data lakehouses store data in open formats (e.g., Parquet, Delta Lake) to ensure flexibility and interoperability, whereas data warehouses often rely on proprietary formats.