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Answer: A star schema with a central fact table for transactions and dimension tables for stores, products, and dates.
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 data, and it is flexible enough to accommodate changes in data characteristics.
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You are tasked with designing a schema for an Amazon Redshift data warehouse that will store transaction data from multiple retail stores. The data includes store IDs, transaction dates, product IDs, quantities sold, and prices. The schema must be optimized for querying large volumes of data and should support future changes in data characteristics. Which of the following schema designs would be most appropriate?
A
A star schema with a central fact table for transactions and dimension tables for stores, products, and dates.
B
A snowflake schema with normalized dimension tables for stores, products, and dates.
C
A single flat table containing all transaction data.
D
A graph database schema to represent relationships between stores and products.