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Answer: Store events in a flat, denormalized structure with precomputed temporal attributes to accelerate pattern matching queries.
In the context of complex event processing within a lakehouse, efficiency is crucial for detecting patterns and executing temporal queries over high-velocity event streams. Storing events in a flat, denormalized structure with precomputed temporal attributes is the most suitable design for maximizing efficiency in this scenario. This approach minimizes redundant data, avoids complex joins, and allows for faster pattern matching queries by leveraging precomputed temporal attributes. It simplifies the process of pattern detection and accelerates query performance, making it the optimal choice for handling high-velocity event streams.
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When modeling data for complex event processing (CEP) within a lakehouse, which design approach is most efficient for detecting patterns and executing temporal queries on high-velocity event streams?
A
Utilize a graph database model within the lakehouse to efficiently map event relationships and patterns.
B
Store events in a flat, denormalized structure with precomputed temporal attributes to accelerate pattern matching queries.
C
Normalize event data across multiple tables to reduce redundancy and facilitate complex joins for pattern detection.
D
Implement custom partitioning and bucketing strategies based on event types and temporal characteristics to optimize query performance.