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 Dataflow streaming pipelines. Raw telemetry flows through JSON parsing, validation, and 5-minute windowing before BigQuery streaming insert, while Cloud Composer orchestrates hourly Dataproc Spark jobs for feature engineering. Vertex AI handles the complete ML lifecycle—training on BigQuery features, storing artifacts in Cloud Storage, and serving predictions via auto-scaling endpoints. This architecture demonstrates production-grade patterns for IoT-to-ML workflows with VPC Service Controls, Cloud KMS encryption, and Cloud Armor DDoS protection. Fork this diagram on Diagrams.so to customize device counts, adjust Dataflow windowing, or swap Vertex AI for custom serving infrastructure.

People also ask

How do I build a real-time IoT analytics pipeline with ML inference on GCP?

This GCP architecture ingests IoT telemetry via Cloud IoT Core and Pub/Sub, processes streams through Dataflow with 5-minute windowing, and stores in BigQuery. Vertex AI trains on engineered features and serves predictions via auto-scaling endpoints.