Databricks Data Engineer Professional Question 41
Single answerA data engineering team is designing a Delta Lake table to store logs generated by an e-commerce platform. The table is expected to receive approximately 500 GB of data daily, and most queries will filter by event_date and region. The team also needs to ensure optimal query performance and data management. Which partitioning strategy is the most appropriate for this scenario?
- A
Partition the table by
event_dateonly - B
Partition the table by
regiononly - C
Partition the table by both
event_dateandregion - D
Do not partition the table, as Delta Lake optimizations will handle performance automatically
Show answer and explanation
Correct answer: C
Explanation
The scenario specifies that most queries will filter by both event_date and region. Partitioning by both columns ensures that the data is organized to optimize query performance for the expected access patterns. While Delta Lake optimizations like Z-ordering can improve performance, they do not replace the benefits of a well-designed partitioning strategy for large-scale data.
- A. Incorrect.
Partitioning by
event_dateonly may help with filtering by date, but it will not optimize queries that filter byregion, leading to potential performance bottlenecks. - B. Incorrect.
Partitioning by
regiononly would optimize queries filtering byregion, but it would not handle the more common filtering onevent_date, leading to suboptimal performance. - C. Correct.
Partitioning by both
event_dateandregionallows the table to optimize queries that filter by both columns, which aligns with the query patterns described. This approach balances query performance and data management effectively. - D. Incorrect.
Not partitioning the table would lead to poor query performance, especially for large datasets, as Delta Lake optimizations like Z-ordering cannot fully replace the benefits of effective partitioning.