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Answer: Partition data by country or region and replicate partitions across global data centers to localize data access.
Partitioning data by country or region and replicating these partitions across global data centers is the optimal strategy for minimizing query latency in a global-scale analytics platform. This approach ensures that data is stored close to the teams that need it, significantly reducing the time required to access and analyze data. Additionally, this strategy supports scalability, compliance with local data sovereignty laws, and enhances fault tolerance by providing data redundancy across multiple locations.
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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.
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