Answer-first summary for fast verification
Answer: Apply Z-order optimization to columns that are often used in query conditions.
**B. Apply Z-order optimization to columns that are often used in query conditions.** This method reorganizes data physically to cluster related information, thereby optimizing query performance by minimizing the data scanned during reads. It's particularly effective for improving read performance without affecting data freshness. While converting to parquet (option C) might offer some performance benefits, it lacks the targeted efficiency of Z-ordering. Increasing partitions (option A) can aid in parallelizing reads but may introduce additional overhead. In-memory caching (option D) boosts performance for repeated queries but is less efficient for large tables or frequent updates, making Z-order optimization the optimal choice for this scenario.
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You observe a slowdown in data read performance on a large Delta Lake table in Databricks. What strategy would you employ to enhance read performance while ensuring the data remains up-to-date?
A
Increase the number of partitions in the Delta table to distribute read operations more evenly.
B
Apply Z-order optimization to columns that are often used in query conditions.
C
Switch the Delta table to parquet format and access the data directly from parquet files.
D
Store the Delta table in memory prior to executing read operations.
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