Explanation:
Partitioning by customer ID and transaction date allows for efficient querying of individual customer data and time-based analyses, which are common in transaction datasets. This strategy also helps in managing storage costs by enabling easy archiving of older data.
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You are tasked with designing a partition strategy for a large dataset of customer transactions stored in Azure Data Lake Storage Gen2. The dataset is expected to grow exponentially and needs to support both batch and real-time analytical queries. How would you implement a partition strategy to optimize query performance and manage storage costs?
A
Partition by customer ID and transaction date
B
Partition by transaction type and amount
C
Partition by geographical region and transaction date
D
Partition by transaction date and time
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