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In a lakehouse architecture requiring frequent and complex aggregations (e.g., rolling averages, cumulative sums) across large datasets, which strategy ensures the most efficient query performance?
A
Dynamically compute aggregations on-the-fly using Spark‘s advanced analytics functions, caching results where possible.
B
Create materialized views that automatically refresh at predefined intervals, storing intermediate aggregation results.
C
Leverage a combination of Delta Lake‘s time travel feature and Spark‘s window functions to compute and store aggregations incrementally.
D
Precompute and store all possible aggregations in separate Delta tables, updating them incrementally.
