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Answer: Pre-aggregate data into a set of materialized views covering common query patterns.
Pre-aggregating data into materialized views for common query patterns is the most efficient method to balance query performance in multi-dimensional data analysis within a lakehouse. This approach allows for faster query responses by providing pre-computed summary tables, reducing the need for on-the-fly data processing. Materialized views can be updated to reflect changes in the underlying data, ensuring accuracy. This method outperforms others by minimizing computational overhead and avoiding the inefficiencies of on-the-fly aggregation or the complexity introduced by overly normalized schemas.
Author: LeetQuiz Editorial Team
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When designing a data model for multi-dimensional data analysis (e.g., OLAP cubes) in a lakehouse, which approach ensures optimal query performance across various aggregation queries?
A
Store raw event data in a flat structure, applying on-the-fly aggregation during each query.
B
Implement a snowflake schema, normalizing dimensions to reduce data redundancy and storage costs.
C
Pre-aggregate data into a set of materialized views covering common query patterns.
D
Utilize a star schema with fact tables and dimension tables, leveraging Delta Lake‘s performance features.
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