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CASE STUDY: ManuIoT
Overview:
Industry: Manufacturing
Size: 100 factories globally
Environment:
- 100,000 sensors
- Local SCADA
- Fragmented SQL Server DBs
- No central analytics
Requirements:
- Predictive maintenance
- Real-time global dashboards
- Edge computing
Exec Statements:
- CEO: Monetize telemetry.
- CFO: Costs must scale linearly.
- VP Ops: Factory lines need local control if internet drops.
Tech Reqs:
- Ingest 1M msgs/sec
- Stream processing
- Offline factory capabilities
- Train ML centrally, deploy to edge
Constraints:
- Low bandwidth/high latency at factories
- Legacy MQTT protocol
- Zero IT staff at factories
QUESTION: How should you architect the ingestion layer to handle 1 million MQTT messages per second from the legacy sensors?
GCP PCA · Question 18 · Resilience Procedures
CASE STUDY: ManuIoT
Overview:
Industry: Manufacturing
Size: 100 factories globally
Environment:
- 100,000 sensors
- Local SCADA
- Fragmented SQL Server DBs
- No central analytics
Requirements:
- Predictive maintenance
- Real-time global dashboards
- Edge computing
Exec Statements:
- CEO: Monetize telemetry.
- CFO: Costs must scale linearly.
- VP Ops: Factory lines need local control if internet drops.
Tech Reqs:
- Ingest 1M msgs/sec
- Stream processing
- Offline factory capabilities
- Train ML centrally, deploy to edge
Constraints:
- Low bandwidth/high latency at factories
- Legacy MQTT protocol
- Zero IT staff at factories
QUESTION: To satisfy the VP of Operations' requirement for offline factory capabilities and local control, which solution should you deploy at the factories?
CASE STUDY: ManuIoT
Overview:
Industry: Manufacturing
Size: 100 factories globally
Environment:
- 100,000 sensors
- Local SCADA
- Fragmented SQL Server DBs
- No central analytics
Requirements:
- Predictive maintenance
- Real-time global dashboards
- Edge computing
Exec Statements:
- CEO: Monetize telemetry.
- CFO: Costs must scale linearly.
- VP Ops: Factory lines need local control if internet drops.
Tech Reqs:
- Ingest 1M msgs/sec
- Stream processing
- Offline factory capabilities
- Train ML centrally, deploy to edge
Constraints:
- Low bandwidth/high latency at factories
- Legacy MQTT protocol
- Zero IT staff at factories
QUESTION: To satisfy the VP of Operations' requirement for offline factory capabilities and local control, which solution should you deploy at the factories?
Answer options:
Google Distributed Cloud Edge (Anthos Bare Metal)
Compute Engine instances connected via Cloud VPN
Cloud Run for Anthos
Local SQL Server databases with asynchronous replication to Cloud SQL
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