Building an IoT Event Hub on Google Cloud
An IoT Event Hub is a central system that ingests, processes, transforms, stores, and routes IoT device events in real time. Google Cloud provides a powerful, scalable, and cost-efficient ecosystem for building such a platform using managed and serverless services.
This guide walks you through a reference architecture, key services, ingestion patterns, processing choices, storage strategies, best practices, and a sample implementation.
🔶 1. Core Requirements of an IoT Event Hub
A robust IoT Event Hub must support:
✔ High-volume event ingestion
Millions of sensor messages per second.
✔ Flexible connectivity options
HTTP, MQTT, WebSockets, etc.
✔ Real-time processing
Filtering, validation, enrichment, anomaly detection.
✔ Scalable storage
Cold, warm, and hot paths for various use cases.
✔ Downstream routing
APIs, analytics tools, ML models, dashboards.
✔ Security & identity management
Device authentication, encryption, key rotation.
Google Cloud provides all of these capabilities via native, serverless services.
🔶 2. Reference Architecture Overview
A modern IoT Event Hub on Google Cloud typically looks like this:
Devices → IoT Core Alternative (MQTT Bridge / Custom) → Pub/Sub → Event Processing Layer → Storage / Analytics / ML
Components:
Layer Google Cloud Services
Ingestion Pub/Sub, HTTPS endpoints, Load Balancer, Cloud Run, MQTT brokers
Processing Cloud Run, Cloud Functions, Dataflow, Vertex AI
Storage BigQuery, Cloud Storage, Firestore, Bigtable
Management Cloud IAM, Secret Manager, Cloud Logging, Monitoring
Delivery Pub/Sub topics, Eventarc, APIs, BigQuery
🔶 3. Designing the Ingestion Layer
Since Cloud IoT Core is retired, here are the recommended ingestion approaches:
⭐ Option A: Pub/Sub as the Ingestion Backbone (Recommended)
Device → MQTT Broker → Pub/Sub
Device → HTTPS POST → Cloud Run → Pub/Sub
Benefits:
Serverless & autoscaling
High throughput (~millions msg/sec)
Durable and replayable
Great for multi-region IoT fleets
⭐ Option B: Cloud Run as a Direct Ingestion API
Create an HTTPS endpoint for devices to send telemetry:
Device → HTTPS → Cloud Run → Pub/Sub
Supports:
JSON
Binary payloads
Message signing
⭐ Option C: Lightweight MQTT Bridge in GKE or Cloud Run
If devices require strict MQTT:
Deploy an MQTT broker like Eclipse Mosquitto or EMQX
Bridge outbound messages → Pub/Sub
🔶 4. Real-Time Processing Layer
Once IoT events land in Pub/Sub, you can fan out processing:
⭐ Cloud Run (Serverless processing)
Use Cloud Run to:
Validate device payloads
Apply transformations
Enrich data (e.g., device metadata lookup)
Route events
Pros: Fast, cheap, stateless, event-driven.
⭐ Cloud Functions (Micro event handlers)
For lightweight workloads such as:
Format normalization
Alert triggers
Device state machine changes
⭐ Dataflow (Streaming ETL / Analytics)
Best for:
High-volume sensor streams
Windowing, aggregation
Complex per-device processing
ML inference (via Vertex AI / TensorFlow)
⭐ Vertex AI for IoT ML
Typical ML use cases:
Predictive maintenance
Anomaly detection
Sensor data forecasting
Image/audio signal processing
Inference can run:
In Dataflow
In Cloud Run
Or via Vertex AI Endpoints
🔶 5. Storage Layer (Hot / Warm / Cold Paths)
The IoT Event Hub typically uses a tiered storage architecture:
🔥 Hot Storage (milliseconds retrieval)
Bigtable
High-speed reads/writes
Suitable for time-series IoT data
Real-time dashboards
🌤 Warm Storage (seconds retrieval)
BigQuery
Analytical queries
Time-series aggregation
Business dashboards
Best for:
Historical analysis
Fleet monitoring
Reporting
❄️ Cold Storage (cheapest)
Cloud Storage
Raw telemetry storage
Backup & archival
Parquet/ORC files for cost-efficient analytics
🔶 6. Routing & Event Distribution
Once processed, events can be routed to:
✔ Other Pub/Sub topics
✔ BigQuery tables
✔ Cloud Storage buckets
✔ Alerting systems (PagerDuty, Slack, etc.)
✔ Downstream APIs using Cloud Run
✔ Real-time dashboards (Data Studio / Looker / Grafana)
Use Eventarc for event-driven routing across Google Cloud.
🔶 7. Example Architecture (Recommended Pattern)
Telemetry Path
Device → MQTT/HTTPS → Cloud Run → Pub/Sub (ingestion)
→ Cloud Run/Dataflow (processing)
→ Bigtable (hot) / BigQuery (warm) / Cloud Storage (cold)
Command & Control Path
Control Application → Pub/Sub → MQTT Broker → Device
Monitoring & Security
IAM → Device Identity
Secret Manager → Keys
Cloud Logging → Event logs
Cloud Monitoring → Fleet metrics
Security → VPC, Firewall, Token-based auth
🔶 8. Sample Implementation: Cloud Run → Pub/Sub IoT Ingestion API
main.py (FastAPI example)
from fastapi import FastAPI, Request
from google.cloud import pubsub_v1
import json
import os
app = FastAPI()
publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path(
os.environ["PROJECT_ID"],
os.environ["TOPIC_NAME"]
)
@app.post("/ingest")
async def ingest_data(request: Request):
data = await request.json()
message = json.dumps(data).encode("utf-8")
publisher.publish(topic_path, message)
return {"status": "ok"}
Deployment
gcloud run deploy iot-ingest \
--source . \
--region us-central1 \
--allow-unauthenticated
Devices can now POST:
curl -X POST https://your-url/ingest \
-H "Content-Type: application/json" \
-d '{"device":"sensor-1","temp":25.3}'
🔶 9. Security Best Practices
✔ Device Authentication
OAuth tokens
API keys
Mutual TLS
Signed messages
✔ Data Encryption
TLS in transit
CMEK for at-rest encryption
✔ Identity & IAM
Use service accounts with least privilege.
✔ Per-device rate limiting
Cloud Armor or API Gateway.
✔ Audit logging
Enable Cloud Audit Logs for all services.
🔶 10. Operational Best Practices
✔ Use Pub/Sub dead-letter topics
✔ Use autoscaling Cloud Run with minimum instances for low latency
✔ Store raw messages before transformation (for replay safety)
✔ Keep ingestion endpoints stateless
✔ Use BigQuery partitioned tables
✔ Add retries/backoff for device communication
⭐ Summary
A complete IoT Event Hub on Google Cloud uses:
✔ Pub/Sub → ingestion backbone
✔ Cloud Run / Dataflow → processing and streaming analytics
✔ Bigtable / BigQuery / Cloud Storage → multi-tiered storage
✔ Eventarc → routing
✔ IAM + Secret Manager → security
✔ Logging/Monitoring → observability
The result is a scalable, event-driven IoT platform that handles millions of messages with low cost and high reliability.
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