GCP Professional Data Engineer Practice Question

A ride-hailing platform needs to persist GPS pings from about 500 000 vehicles, each pinging once per second. The matching microservice must retrieve the most recent location for any driver within 50 ms to update nearby driver lists, while data engineers run hourly aggregate queries for surge-pricing models. The team wants minimal operational overhead and automatic horizontal scaling. Which Google Cloud storage service should be the system of record for the pings?

Stream each ping directly into partitioned BigQuery tables and query the latest row in real time.
Insert pings into Cloud SQL for PostgreSQL with high-availability and multiple read replicas.
Store the pings in Cloud Bigtable using a composite row key, then export to BigQuery for hourly analytics.
Publish each ping to Cloud Pub/Sub with seven-day retention and let services pull messages when needed.

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Answer Description

Cloud Bigtable is designed for very high-throughput, low-latency workloads. With a carefully designed row key such as driverId#timestamp, it delivers single-digit millisecond reads and writes and scales linearly by adding nodes, satisfying the real-time lookup requirement for the matching service. Dataflow or BigQuery can consume the same table periodically for analytical reporting. BigQuery streaming offers fast ingestion but cannot guarantee sub-50 ms per-row retrieval. Pub/Sub is a messaging bus, not a low-latency store, and Cloud SQL cannot horizontally scale to hundreds of thousands of writes per second without significant re-architecture.

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