Databricks Data Engineer Associate Question 5
Single answerA data engineering team is tasked with designing a unified data platform for their organization using Databricks. The team must ensure the platform supports structured, semi-structured, and unstructured data, allows collaborative data science and machine learning, and provides support for real-time and batch processing. Which feature of the Databricks Lakehouse Platform makes it suitable for addressing all these requirements?
- A
Delta Lake
- B
Integrated Apache Spark runtime
- C
Support for SQL, Python, R, and Scala in a collaborative notebook environment
- D
Unified storage and compute layers
Show answer and explanation
Correct answer: D
Explanation
The Databricks Lakehouse Platform is designed to combine the best features of data lakes and data warehouses with support for a variety of data types, workloads, and collaboration. The unified storage and compute layers play a central role in meeting the diverse needs of data engineering, data science, and analytics teams, making it the correct answer for this scenario.
- A. Incorrect.
Delta Lake is a key component of the Databricks Lakehouse Platform, but it primarily focuses on enabling ACID transactions, data versioning, and handling structured and semi-structured data. It does not inherently address all aspects of a unified platform, such as collaborative environments or unstructured data support.
- B. Incorrect.
The integrated Apache Spark runtime is a significant feature of Databricks that supports scalable data processing for real-time and batch workloads. However, it is not the defining feature that unifies storage, compute, and analytics capabilities in the platform.
- C. Incorrect.
Support for multiple programming languages in collaborative notebooks is an important feature for data teams. However, it is not the core reason why Databricks Lakehouse Platform meets the diverse requirements of a unified data platform.
- D. Correct.
The unified storage and compute layers in the Databricks Lakehouse Platform allow seamless handling of structured, semi-structured, and unstructured data, while supporting real-time and batch processing and enabling collaborative data science and machine learning workflows. This is the key feature that addresses all the requirements described in the scenario.