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Answer: Use a distributed processing framework like Spark to process the data, perform real-time decision making, create windowed aggregates, and handle schema drift.
Option D is the correct approach as it covers all the necessary components for processing data with high variability and real-time decision making. It includes using a distributed processing framework like Spark, performing real-time decision making, creating windowed aggregates, and handling schema drift. This approach ensures that the solution can handle the variability of the data and provide accurate insights for real-time decision making.
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You are working on a stream processing solution that requires processing data with a high degree of variability and requires real-time decision making. How would you approach this task to ensure efficient and accurate processing?
A
Use a simple processing approach to handle the variability of the data.
B
Use a distributed processing framework like Spark to process the data and perform real-time decision making.
C
Use a distributed processing framework like Spark to process the data, perform real-time decision making, and create windowed aggregates.
D
Use a distributed processing framework like Spark to process the data, perform real-time decision making, create windowed aggregates, and handle schema drift.