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Answer: Transform the POST request's JSON data, and stream it into BigQuery.
The requirement is for immediate availability of data for complex analytics with parallel user access. BigQuery (Option B) is designed for large-scale analytics and supports streaming inserts, enabling real-time data availability. It efficiently handles complex queries and concurrent access. Datastore (A) is a NoSQL database suited for transactional workloads, not analytics. Cloud SQL (C) is a relational database but lacks the scalability for large analytics workloads. Storing individual files in Cloud Storage (D) would require additional processing to query the data, introducing latency.
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What is the recommended approach for persisting data in an HTTP Cloud Function triggered by POST requests, where each submission contains a flat JSON structure with numeric and text fields, to ensure the collected data is immediately available for concurrent complex analytics by multiple users?
A
Directly persist each POST request's JSON data into Datastore.
B
Transform the POST request's JSON data, and stream it into BigQuery.
C
Transform the POST request's JSON data, and store it in a regional Cloud SQL cluster.
D
Persist each POST request's JSON data as an individual file within Cloud Storage, with the file name containing the request identifier.
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