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Answer: Implement a star schema with a time dimension table that includes various time hierarchies.
The correct answer is **C. Implement a star schema with a time dimension table that includes various time hierarchies.** This approach is optimal because: 1. **Star Schema Efficiency**: A star schema organizes data into a central fact table linked to dimension tables, including a time dimension. This setup is ideal for time-series data, enabling quick queries and aggregations. 2. **Time Hierarchies**: Incorporating hierarchies (like year, month, day) in the time dimension allows for flexible data analysis at various granularities, essential for detailed time-based queries. 3. **Performance Benefits**: The star schema's structure supports efficient data filtering and aggregation by time, significantly improving query performance. 4. **Data Integrity**: This model ensures accurate relationships between time, events, and entities, crucial for reliable analysis and reporting. In summary, a star schema with a detailed time dimension table is the best choice for managing time-series data in a lakehouse, offering both performance and flexibility.
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When designing a data model for time-series data in a lakehouse, which approach is most effective for optimizing query performance for time-based aggregations?
A
Store raw event data and use views to dynamically aggregate data on the-fly.
B
Use a flat wide table structure that pre-aggregates data at daily intervals.
C
Implement a star schema with a time dimension table that includes various time hierarchies.
D
Normalize the data into multiple dimension tables related to time, events, and entities.
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