Databricks Data Engineer Associate Question 418
Select 3You are tasked with implementing a production data pipeline in Databricks that processes streaming data coming from IoT devices. The pipeline must ensure data reliability, scalability, and fault tolerance. Which of the following steps should you include to achieve these goals?
- A
Use Delta Lake to store the streaming data with ACID transactions enabled.
- B
Leverage Auto Loader to efficiently ingest streaming data from cloud object storage.
- C
Use static tables instead of Delta Lake to simplify the pipeline architecture.
- D
Configure checkpointing to maintain state and recovery during failures.
- E
Write custom retry logic for job failures instead of relying on built-in Databricks features.
Show answer and explanation
Correct answers: A, B, D
Explanation
To implement a reliable, scalable, and fault-tolerant production pipeline in Databricks for streaming data, you should leverage Delta Lake for ACID compliance, Auto Loader for efficient ingestion, and checkpointing for state management and recovery. These tools are designed to handle production workloads effectively, while static tables and custom retry logic do not align with best practices for modern pipeline design.
- A. Correct.
Delta Lake is essential for ensuring data reliability and fault tolerance because it supports ACID transactions and scalable data storage.
- B. Correct.
Auto Loader is designed to efficiently handle streaming data ingestion from cloud storage, making it a scalable and reliable solution.
- C. Incorrect.
Static tables do not provide the same level of reliability, scalability, or fault tolerance as Delta Lake, making them unsuitable for production streaming pipelines.
- D. Correct.
Checkpointing is crucial for maintaining state and enabling recovery from failures in streaming pipelines.
- E. Incorrect.
Databricks provides built-in fault-tolerance mechanisms, so writing custom retry logic is unnecessary and could lead to additional complexity.