Databricks Data Engineer Professional Question 44
Single answerYou are designing a batch processing pipeline in Databricks to process sales data stored in a Delta Lake table. The table is partitioned by region. During the pipeline development, you notice that the job processing time is significantly increasing as the data volume grows. What is the most effective optimization strategy to improve the pipeline's performance while keeping the existing partitioning scheme?
- A
Use the OPTIMIZE command with ZORDER on frequently queried columns.
- B
Increase the number of partitions on the Delta Lake table.
- C
Repartition the data by a different column instead of
region. - D
Manually compact small files in the Delta Lake table.
Show answer and explanation
Correct answer: A
Explanation
The OPTIMIZE command with ZORDER is a Delta Lake feature specifically designed to optimize data layout within partitions, enhancing query performance without altering the existing partitioning scheme. It minimizes read amplification and improves scan efficiency for frequently queried columns, making it the most effective solution in this scenario.
- A. Correct.
Using the OPTIMIZE command with ZORDER organizes the data within each partition, improving query performance by co-locating frequently queried data. This is the best choice for optimizing a Delta Lake table without altering the existing partitioning scheme.
- B. Incorrect.
Increasing the number of partitions may unnecessarily introduce overhead when reading or writing data, especially if the current partitions are already well-distributed.
- C. Incorrect.
Repartitioning the data by a different column would require significant restructuring and could potentially degrade query performance for existing queries optimized for the
regionpartitioning scheme. - D. Incorrect.
Manually compacting small files can help with performance but is less efficient and more error-prone compared to using the OPTIMIZE command.