GCP Professional Data Engineer Practice Question

You are designing a streaming pipeline that ingests temperature readings from tens of thousands of IoT devices through Pub/Sub and must persist the data in a storage tier that can sustain millions of writes per second while offering sub-10-millisecond read latency for lookups by device-id and event timestamp to power a real-time dashboard. The data is append-only and will not be queried with complex joins or multi-row transactions. Which Google Cloud sink best meets these requirements with minimal operational overhead?

Cloud Pub/Sub Lite topic configured with 7-day message retention
Cloud Spanner table keyed on device-id with a secondary index on timestamp
BigQuery partitioned table using ingestion-time partitioning
Cloud Bigtable with a composite row key of device-id and reversed timestamp

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Answer Description

Cloud Bigtable is engineered for very high write throughput and single-row reads in single-digit milliseconds, making it ideal for time-series or sensor data that is appended continuously and retrieved by a well-designed row key such as device-id#timestamp. BigQuery is optimized for analytical scans, not low-latency point reads. Cloud Spanner provides strong relational semantics and distributed transactions, which add cost and complexity unnecessary for this append-only workload. Pub/Sub Lite is a messaging service rather than a serving store and is not intended for low-latency random access queries.

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