A media-streaming company wants to update video recommendations while users are still watching, based on each viewer's most recent clicks and search terms. Which benefit of real-time streaming analytics makes this use case possible?
Processing can be deferred to nightly jobs, removing the need for continuous resource consumption.
Analytics results become more statistically accurate because only large, complete datasets are examined.
Insights are produced as data arrives, allowing applications to act within the same user session.
Historical data retention periods can be greatly extended without increasing storage cost.
Streaming analytics processes events the moment they are generated, so insights are available with sub-second latency. This enables applications-such as recommendation engines-to react immediately to user behavior. Batch processing cannot influence the current session because data is analyzed only after the load jobs complete. Storing data in cheaper archival tiers lowers cost but does not provide timely insight, and merely enlarging historical datasets does not shorten feedback loops.
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