In a streaming workload scenario, you are required to process real-time data from social media feeds. The data includes text, user information, and timestamp. What partition strategy would you implement to handle the high volume and velocity of this data effectively?

Simulated

Explanation:

Partitioning by language and timestamp helps in managing the diversity and volume of social media data. This strategy allows for efficient processing of data based on language preferences and chronological order, which are critical for real-time analytics.

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Microsoft Azure Data Engineer Associate - DP-203

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Microsoft Azure Data Engineer Associate - DP-203

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In a streaming workload scenario, you are required to process real-time data from social media feeds. The data includes text, user information, and timestamp. What partition strategy would you implement to handle the high volume and velocity of this data effectively?