Associate Data Practitioner · Free Practice Question Medium
Question 13
A company wants to analyze streaming IoT data in real time while maintaining a historical archive for compliance. Which storage architecture would best balance cost and performance?
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A
Pub/Sub → BigQuery for real-time analysis + Cloud Storage for archives
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B
Pub/Sub → Bigtable for real-time + BigQuery for historical
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C
IoT Core → Cloud SQL for real-time + Cloud Storage for archives
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D
Dataflow → Firestore for real-time + BigQuery for historical
Reveal correct answer
Correct answer: B
Explanation
Option B is CORRECT. Bigtable provides low-latency access for real-time IoT analytics with high write throughput, while BigQuery offers cost-effective storage for historical analysis. This architecture separates hot (recent) and cold (historical) data appropriately: Bigtable handles millisecond-level queries on current data, and BigQuery’s columnar storage optimizes analytical queries on historical data. Data can be periodically exported from Bigtable to BigQuery for long-term retention.
Option A is INCORRECT. While BigQuery can ingest streaming data, it’s not optimized for high-volume real-time queries. Directly streaming to BigQuery would incur higher costs and latency for operational queries compared to using Bigtable as the real-time layer.
Option C is INCORRECT. Cloud SQL is a relational database unsuitable for high-velocity IoT data ingestion. It lacks the scalability to handle thousands of writes per second and would become a bottleneck for real-time processing.
Option D is INCORRECT. Firestore is a document database designed for mobile/web apps, not high-volume time-series IoT data. It lacks the write throughput and query patterns needed for real-time IoT analytics.
Discussion
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