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Answer: Use Spark structured streaming to read the data from a storage account, perform real-time processing, create windowed aggregates, and handle schema drift.
Option D is the correct approach as it covers all the necessary components for processing time series data using Spark structured streaming. It includes reading the data in real-time, performing real-time processing, creating windowed aggregates, and handling schema drift. This approach ensures that the solution can handle the time-sensitive nature of time series data and provide insights into trends and patterns.
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You are working on a project that requires processing time series data using Spark structured streaming. The data is being ingested in real-time and needs to be processed and analyzed for trends and patterns. How would you approach this task?
A
Use Spark structured streaming to read the data from a storage account and perform batch processing.
B
Use Spark structured streaming to read the data from a storage account and perform real-time processing.
C
Use Spark structured streaming to read the data from a storage account, perform real-time processing, and create windowed aggregates.
D
Use Spark structured streaming to read the data from a storage account, perform real-time processing, create windowed aggregates, and handle schema drift.