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Answer: A star schema with a central fact table for interactions and dimension tables for customers, events, and sessions.
A star schema is the most appropriate for this scenario as it optimizes for query performance by denormalizing the data into a central fact table and separate dimension tables. This design allows for efficient querying and aggregation of large volumes of interaction data, and it is flexible enough to accommodate changes in interaction data characteristics.
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You are designing a schema for an Amazon Redshift data warehouse that will store customer interaction data from a web application. The data includes customer IDs, interaction timestamps, event types, and session IDs. The schema must be optimized for analyzing customer behavior and should support future changes in interaction data characteristics. Which of the following schema designs would be most appropriate?
A
A star schema with a central fact table for interactions and dimension tables for customers, events, and sessions.
B
A snowflake schema with normalized dimension tables for customers, events, and sessions.
C
A single flat table containing all interaction data.
D
A graph database schema to represent relationships between customers and events.