Databricks Data Engineer Professional Question 155
Select 3You are designing a data pipeline in Databricks to process streaming data from multiple multiplex bronze tables, each containing raw data from different sources. Which of the following best practices should you follow to ensure efficient and reliable streaming processing?
- A
Use schema evolution to handle changes in the incoming data structure automatically.
- B
Combine all multiplex bronze streams into a single stream to reduce complexity.
- C
Apply schema validation to detect and address corrupted or unexpected data early in the pipeline.
- D
Use incremental checkpointing to ensure fault tolerance and enable recovery from failures.
- E
Persist raw data to delta format in bronze tables before performing schema validation.
Show answer and explanation
Correct answers: A, C, D
Explanation
When streaming data from multiplex bronze tables, adhering to best practices is crucial for reliability and efficiency. Schema evolution ensures the pipeline adapts to changes in the incoming data structure dynamically. Schema validation helps detect and address issues early, preventing downstream failures. Incremental checkpointing ensures fault tolerance and recovery capabilities. Combining all bronze streams into one is not recommended due to potential inefficiencies, and while persisting raw data in bronze tables is important, schema validation is typically performed in the silver layer to ensure data quality.
- A. Correct.
Schema evolution is critical when working with streaming data from multiple sources, as it allows the pipeline to handle schema changes dynamically without manual intervention, ensuring continued data ingestion.
- B. Incorrect.
Combining all multiplex bronze streams into a single stream can lead to inefficiencies and complexities in processing. Each source should ideally be handled independently to ensure modularity and scalability.
- C. Correct.
Schema validation is essential for detecting errors early in the pipeline. By validating schemas before further processing, you can prevent downstream failures caused by corrupted or unexpected data.
- D. Correct.
Incremental checkpointing is a best practice in streaming systems to maintain fault tolerance. It allows the pipeline to recover from the last successful checkpoint in case of failure, ensuring no data loss.
- E. Incorrect.
Persisting raw data in bronze tables should occur before any transformations, but schema validation is typically applied to the data as it moves to the silver layer, not in the bronze layer.