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Answer: A star schema with a central fact table for sensor readings and dimension tables for devices, timestamps, and locations.
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 time-series data, and it is flexible enough to accommodate changes in sensor data characteristics.
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You are designing a schema for an Amazon Redshift data warehouse that will store sensor data from IoT devices. The data includes device IDs, timestamps, sensor readings, and location information. The schema must be optimized for time-series analysis and should support future changes in sensor data characteristics. Which of the following schema designs would be most appropriate?
A
A star schema with a central fact table for sensor readings and dimension tables for devices, timestamps, and locations.
B
A snowflake schema with normalized dimension tables for devices, timestamps, and locations.
C
A single flat table containing all sensor data.
D
A graph database schema to represent relationships between devices and sensor readings.
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