Within a typical data value chain, which activity best represents the "process and analyze" stage rather than the "collect", "store", or "govern" stages?
Moving cold backups to the Archive storage class in Cloud Storage
Publishing click-stream events from a website to Pub/Sub topics
Assigning IAM roles to limit access to a BigQuery dataset
Aggregating real-time streaming logs into summarized metrics with Dataflow
The process and analyze stage focuses on transforming raw, stored data into meaningful information that can support decisions. Using a service such as Dataflow to aggregate and enrich streaming logs is an example of this transformation step. Setting IAM roles relates to governance, archiving backups is a storage task, and capturing click-stream events is part of data collection (ingest).
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What is Google Dataflow and how does it aggregate data?
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How does the 'process and analyze' stage differ from 'collect' and 'store' stages in the data value chain?
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What are streaming logs, and why are they useful for analysis?
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What is Dataflow in GCP and how does it help in aggregating real-time streaming logs?
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How does the process and analyze stage differ from the storage stage in the data value chain?
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What does real-time streaming log aggregation look like and why is it important?
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