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Answer: Consider data size, network bandwidth, and SQL pool capacity. Larger batch sizes improve performance but may increase the risk of failures.
The optimal batch size depends on factors such as data size, network bandwidth, and SQL pool capacity. Larger batch sizes can improve performance by reducing the number of transactions, but they may also increase the risk of failures due to larger data chunks.
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You are configuring the batch size for a data loading process into an Azure Synapse Analytics SQL pool using PolyBase. Describe the factors you would consider when determining the optimal batch size. Also, explain how the batch size affects the performance and reliability of the data load process.
A
Consider data size, network bandwidth, and SQL pool capacity. Larger batch sizes improve performance but may increase the risk of failures.
B
Consider data size, network bandwidth, and SQL pool capacity. Smaller batch sizes improve performance but may increase the risk of failures.
C
Consider data size, network bandwidth, and SQL pool capacity. Larger batch sizes improve performance and reduce the risk of failures.
D
Consider data size, network bandwidth, and SQL pool capacity. Smaller batch sizes improve performance and reduce the risk of failures.