About This Architecture

End-to-end GCP IoT analytics platform ingesting 10GB/day from 50,000+ devices through Cloud IoT Core and Pub/Sub into parallel streaming and batch pipelines. Dataflow handles real-time parsing, validation, and 5-minute windowing into BigQuery, while Cloud Composer orchestrates hourly Spark jobs on autoscaling Dataproc for feature engineering. Vertex AI trains anomaly detection models and serves predictions via replicated endpoints, with results flowing back to BigQuery for unified analytics. This architecture demonstrates GCP best practices for lambda-style IoT processing with integrated ML inference at scale. Fork this diagram on Diagrams.so to customize device counts, adjust windowing strategies, or swap Dataproc for Dataflow batch.

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How do I build a real-time IoT analytics pipeline with machine learning on GCP?

This GCP architecture ingests IoT telemetry via Cloud IoT Core and Pub/Sub, processes it through Dataflow streaming and Dataproc batch pipelines, and runs Vertex AI anomaly detection with results unified in BigQuery.