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For a global-scale analytics platform built on a lakehouse, which data partitioning strategy is most effective in reducing query latency for teams spread across different geographical locations?
A
Use hash partitioning on a globally unique identifier to ensure even distribution of data across partitions, regardless of geographic location.
B
Implement range partitioning based on timestamp to optimize for temporal queries common in analytics workloads.
C
Partition data by country or region and replicate partitions across global data centers to localize data access.
D
Rely on the lakehouse‘s default partitioning mechanism, focusing on optimizing network connectivity between regions instead.
