Databricks Data Engineer Professional Question 46
Select 3You are working on a data pipeline in Databricks that processes large volumes of transactional data. The pipeline needs to handle both historical data (batch processing) and real-time updates (incremental processing). After implementing the pipeline, you notice performance issues with query execution. Which of the following steps should you take to optimize the pipeline?
- A
Use Delta Lake for both batch and incremental processing to take advantage of ACID transactions and efficient file management.
- B
Enable Auto Optimize and Auto Compaction on the Delta Lake table to reduce small files and improve query performance.
- C
Switch from Delta Lake to a traditional Parquet-based approach to simplify the storage format.
- D
Implement Z-Ordering on frequently queried columns to improve data skipping and read performance.
- E
Increase the size of the cluster without analyzing the pipeline bottlenecks.
Show answer and explanation
Correct answers: A, B, D
Explanation
The correct options focus on leveraging Delta Lake's features and configurations, such as ACID transactions, Auto Optimize, Auto Compaction, and Z-Ordering, to address performance issues in a pipeline that handles both batch and incremental processing. These steps target common bottlenecks like small files and inefficient reads. Simply increasing cluster size or reverting to traditional storage formats would not address the underlying issues and could lead to inefficient resource utilization.
- A. Correct.
Using Delta Lake is a best practice for handling both batch and incremental processing because it provides ACID transactions, efficient file management, and support for time travel, which are crucial for performance and reliability.
- B. Correct.
Enabling Auto Optimize and Auto Compaction on Delta Lake automatically manages small files and improves query performance, which is a common optimization strategy in data engineering.
- C. Incorrect.
Switching to a Parquet-based approach would negate the benefits of Delta Lake (e.g., ACID transactions, optimized reads), and is not a recommended optimization strategy for this scenario.
- D. Correct.
Z-Ordering is an optimization technique in Delta Lake that improves data skipping and read performance by co-locating related data within storage, which is beneficial for frequently queried columns.
- E. Incorrect.
Increasing the size of the cluster without identifying and addressing the root cause of the performance issue is not an effective optimization strategy and may lead to unnecessary costs.