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Answer: Leverage a combination of Delta Lake‘s time travel feature and Spark‘s window functions to compute and store aggregations incrementally.
In a lakehouse architecture that requires frequent and complex aggregations across large datasets, leveraging a combination of Delta Lake‘s time travel feature and Spark‘s window functions to compute and store aggregations incrementally provides the most efficient query performance. Delta Lake‘s time travel feature allows access to previous versions of data, enabling efficient computation of incremental aggregations without reprocessing the entire dataset. Spark‘s window functions are powerful for performing complex aggregations efficiently, partitioning data into windows based on specified criteria. This approach minimizes full table scans and recomputation of aggregations, ensuring faster query performance and up-to-date aggregations without manual intervention.
Author: LeetQuiz Editorial Team
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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.
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