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Answer: Use a distributed processing framework like Spark to process the data with a high volume and velocity, create windowed aggregates, and handle schema drift.
Option D is the correct approach as it leverages the power of distributed processing frameworks like Spark to efficiently process data with a high volume and velocity. It also includes creating windowed aggregates and handling schema drift, which are essential for ensuring accurate results. This approach ensures that the solution can handle the high volume and velocity of the data and provide accurate insights.
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In a stream processing solution, you need to process data with a high volume and velocity. How would you approach this task to ensure efficient and accurate processing?
A
Use a single-threaded processing approach to handle the high volume and velocity of the data.
B
Use a distributed processing framework like Spark to process the data with a high volume and velocity.
C
Use a distributed processing framework like Spark to process the data with a high volume and velocity and create windowed aggregates.
D
Use a distributed processing framework like Spark to process the data with a high volume and velocity, create windowed aggregates, and handle schema drift.