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CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
Which architecture should you design to handle the ingestion of 500,000 messages per second from vehicles with intermittent connectivity?
GCP PCA · Question 12 · Storage Systems
CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
To address the CFO's concern about spiraling storage costs for petabytes of historical telemetry data, what should you recommend?
CASE STUDY: TerramEarth
Company Overview: TerramEarth manufactures heavy equipment. 2 million vehicles in the field.
Current Environment: Vehicles send telemetry via cellular. Processing 100,000 msgs/sec. On-prem Hadoop cluster.
Business Requirements: Predict equipment failure. Reduce warranty costs. Provide fleet dashboard.
Executive Statements: CEO: 'Monetize data.' CFO: 'Storage costs spiraling.' CTO: 'Need scalable ingestion and ML.'
Technical Requirements: Ingest 500,000 msgs/sec. Store petabytes cost-effectively. Train ML models. Real-time anomaly detection.
Constraints: Intermittent connectivity. Strict vehicle authentication.
QUESTION:
To address the CFO's concern about spiraling storage costs for petabytes of historical telemetry data, what should you recommend?
Answer options:
Store all data in Cloud Spanner to ensure strong consistency.
Store data in Cloud Storage Standard class, and use Object Lifecycle Management to move data older than 90 days to Coldline or Archive.
Store all data in Persistent Disk SSDs attached to Compute Engine instances.
Delete data older than 30 days to save costs.
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