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A global ride-sharing company needs to collect real-time telemetry data from millions of vehicles, process it, and then store it for further analysis. The data stream is continuous and high-volume, requiring a messaging service that can ingest events reliably and a processing service that can transform and enrich this streaming data. Which combination of Google Cloud services would best meet these requirements?
- ACloud Functions and Cloud Storage
- BCloud Pub/Sub and Dataflow
- CCloud Spanner and Compute Engine
- DCloud SQL and App Engine
Show answer & explanationAnswer & explanation
Correct answer: B. Cloud Pub/Sub and Dataflow
Cloud Pub/Sub is a real-time messaging service designed for ingesting high volumes of events. Dataflow is a fully managed service for executing Apache Beam pipelines, ideal for processing and transforming streaming data. This combination is perfect for real-time data ingestion and processing at scale.
Why the other options are wrong
- A. Cloud Functions are for short-lived, event-driven tasks, and Cloud Storage is for object storage, not real-time stream processing.
- C. Cloud Spanner is a transactional database, and Compute Engine provides VMs, neither are designed for real-time streaming ingestion and processing.
- D. Cloud SQL is a relational database, and App Engine is a PaaS for web apps, not suitable for high-volume real-time data streaming and processing.
Cloud Pub/Sub & Dataflow
Cloud Pub/Sub is a messaging service for ingesting real-time events, while Dataflow is a service for processing streaming and batch data.
- Pub/Sub provides scalable, asynchronous messaging.
- Dataflow offers unified stream and batch processing (Apache Beam).
- Commonly used together for real-time data pipelines.
Memory trick: Publish data, flow it through.