Databricks Data Engineer Associate Question 4
Select 3A data engineering team is tasked with building a unified data platform for their organization. They want to ingest batch and streaming data, perform transformations, and enable machine learning workloads while ensuring data governance and scalability. Why should they consider using the Databricks Lakehouse Platform?
- A
It unifies data warehousing and AI/ML workloads on a single platform.
- B
It supports both structured and unstructured data processing, eliminating the need for separate systems.
- C
It is limited to batch processing and does not support streaming workloads.
- D
It provides built-in capabilities for data governance and security.
- E
It requires separate infrastructure for storage and compute, making it less scalable.
Show answer and explanation
Correct answers: A, B, D
Explanation
The Databricks Lakehouse Platform is a unified analytics platform that combines the best features of data lakes and data warehouses. It supports batch and streaming data processing, handles structured and unstructured data, and includes built-in governance and security features. It is designed for scalability and eliminates the need for separate systems for different workloads, making it an ideal choice for organizations seeking a unified data platform.
- A. Correct.
Correct. The Databricks Lakehouse Platform combines the capabilities of data lakes and data warehouses, enabling a unified approach to analytics and AI/ML workloads.
- B. Correct.
Correct. The Lakehouse Platform supports both structured and unstructured data, avoiding the need for separate systems for different data types.
- C. Incorrect.
Incorrect. The Lakehouse Platform supports both batch and streaming data processing, making it versatile for various workloads.
- D. Correct.
Correct. The platform includes built-in features for data governance and security, such as role-based access control and audit logging.
- E. Incorrect.
Incorrect. The Lakehouse Platform is designed to be highly scalable, leveraging cloud-native architecture that separates storage and compute for elasticity and cost efficiency.