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A global manufacturing company needs to collect, process, and analyze real-time sensor data from thousands of IoT devices deployed worldwide. The data stream is continuous and high-volume, and they require immediate insights for anomaly detection and operational monitoring. Which architectural pattern should they implement on Google Cloud?
- AData lake with Cloud Storage and Dataproc
- BRelational database with Cloud SQL and Looker Studio
- CReal-time stream processing pipeline with Pub/Sub, Dataflow, and BigQuery
- DBatch processing with Cloud Storage and BigQuery
Show answer & explanationAnswer & explanation
Correct answer: C. Real-time stream processing pipeline with Pub/Sub, Dataflow, and BigQuery
For high-volume, continuous real-time data streams requiring immediate insights, a real-time stream processing pipeline is essential. Pub/Sub handles ingestion, Dataflow performs real-time processing and transformations, and BigQuery stores and analyzes the streaming data for immediate insights.
Why the other options are wrong
- A. A data lake with Cloud Storage and Dataproc is typically used for large-scale batch processing and complex data transformations, not for real-time stream processing requiring immediate insights.
- B. A relational database like Cloud SQL is not designed for the scale and throughput of 'thousands of IoT devices' generating 'high-volume, continuous' data streams for real-time analytics.
- D. Batch processing is suitable for historical data analysis, not for 'real-time' data streams requiring 'immediate insights'.
Real-time Stream Processing Pipeline
An architectural pattern for processing continuous, high-volume data streams as they arrive, enabling immediate analysis, anomaly detection, and operational insights.
- Ingestion: Pub/Sub (scalable messaging)
- Processing: Dataflow (serverless stream/batch processing)
- Storage/Analysis: BigQuery (serverless data warehouse for streaming data)
- Enables immediate insights and reactions
Memory trick: Pub/Sub > Dataflow > BigQuery: Stream to Insight, Instantly.