A data analytics team retrieves information from a remote service each day through an API for daily visualization updates. The remote provider enforces a limit on the number of requests per hour. In recent weeks, the team has seen more error messages when usage limits are pushed too high. Which method reduces these errors while preserving all requested data?
Change requests to a different transfer format to lower overhead
Use a queue-based method that schedules calls within enforced request limits
Add more parallel threads to collect data at a faster rate
Enable caching to bypass the remote service calls for daily retrieval
A queue-based method regulates how frequently calls are sent, which prevents exceeding service-request thresholds. Increasing parallel threads raises the risk of additional rejections. Changing the data format does not address the issue of exceeding limits. Caching existing data can help with repeated queries against local content, but it does not fetch fresh data needed for daily updates on its own.
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