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Answer: Use a distributed processing framework like Spark to process the data across multiple partitions.
Option C is the correct approach as it leverages the power of distributed processing frameworks like Spark to efficiently process data across multiple partitions. This approach ensures that the solution can handle large volumes of data and provide accurate results. Option D is incorrect because handling schema drift is not directly related to processing data across partitions.
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In a stream processing solution, you need to process data across multiple partitions. How would you approach this task to ensure efficient and accurate processing?
A
Use a single partition to process all the data and ignore the partitioning.
B
Distribute the data across multiple partitions and process each partition independently.
C
Use a distributed processing framework like Spark to process the data across multiple partitions.
D
Use a distributed processing framework like Spark to process the data across multiple partitions and handle schema drift.
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