Answer: Partition the data by the most frequently queried dimension and use Z-order clustering on secondary dimensions.

Partitioning the data by the most frequently queried dimension allows for data to be physically organized in a way that aligns with the most common access patterns, reducing the amount of data that needs to be scanned during queries. This can significantly improve query performance by minimizing the amount of data that needs to be processed. Additionally, using Z-order clustering on secondary dimensions further optimizes query performance by arranging data in a way that preserves locality, meaning that related data points are stored close to each other on disk. This can reduce the amount of data that needs to be read from disk during queries, further reducing query latency. Creating multiple materialized views (option A) can be resource-intensive and may not be efficient for a dataset with billions of rows. Utilizing a single, flat table structure with extensive indexing on all queryable dimensions (option B) can lead to increased storage overhead and slower query performance due to the need to maintain and update multiple indexes. Applying graph-based data modeling within Delta tables (option D) may not be the most efficient approach for multi-dimensional clustering, as graph-based modeling is more suited for representing relationships between entities rather than optimizing query performance for multi-dimensional data. Overall, partitioning the data by the most frequently queried dimension and using Z-order clustering on secondary dimensions is the most suitable data modeling technique for minimizing query latency across diverse query patterns on a lakehouse dataset with billions of rows.

Author: LeetQuiz Editorial Team