Databricks Data Engineer Associate Question 2
Single answerA data engineering team is using the Databricks Lakehouse Platform to manage their data pipeline. They want to ensure that data from multiple sources is ingested, processed, and stored efficiently while maintaining scalability and supporting advanced analytics workloads. Which key feature of the Databricks Lakehouse Platform enables this seamless integration of structured, semi-structured, and unstructured data?
- A
Unified storage and compute in a single layer
- B
Support for Delta Lake as a storage layer
- C
Built-in machine learning model hosting
- D
Integration with external data warehouses
Show answer and explanation
Correct answer: B
Explanation
The Databricks Lakehouse Platform leverages Delta Lake as a foundational storage layer, which allows for efficient management of structured, semi-structured, and unstructured data. Delta Lake provides features like ACID transactions, schema enforcement, and scalability, making it a key feature for seamless data integration and analytics workflows.
- A. Incorrect.
Databricks Lakehouse Platform separates storage and compute rather than unifying them in a single layer. This ensures scalability but is not the specific feature enabling seamless integration of multiple data types.
- B. Correct.
Delta Lake is a critical feature of the Databricks Lakehouse Platform that provides support for ACID transactions, schema enforcement, and time travel, enabling efficient handling of structured, semi-structured, and unstructured data across the pipeline.
- C. Incorrect.
While the Databricks Lakehouse Platform supports machine learning, built-in model hosting is not directly related to handling data integration and processing across multiple data types.
- D. Incorrect.
Integration with external data warehouses is supported by Databricks but is not the defining feature enabling efficient ingestion, processing, and storage of multiple data types in the Lakehouse Platform.