Explanation:
Partitioning by device ID and timestamp ensures that data from each device is grouped together, facilitating efficient querying and processing. This strategy also supports scalability by allowing the system to handle increasing data volumes as more devices are added.
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Consider a scenario where you are working with Azure Synapse Analytics to process large-scale analytical workloads. The data includes sensor readings from multiple devices, each recording different metrics at varying intervals. What partition strategy would you implement to ensure optimal performance and scalability?
A
Partition by device ID and timestamp
B
Partition by metric type and value range
C
Partition by device type and timestamp
D
Partition by timestamp and location