CASE STUDY: AeroMech
Overview: Aviation manufacturer, 5000 employees, $2B revenue. 100 engines, 10k sensors/engine, 1GB data/flight. On-prem Hadoop.
Business Req: Predictive maintenance, secure data sharing with airlines, monetize data.
Execs: CEO wants new revenue; CFO demands ML ROI; CTO says on-prem storage unfeasible.
Tech Req: High-throughput ingestion, PB-scale storage, train ML on historical data, deploy ML to edge (aircraft).
Constraints: Intermittent low-bandwidth flight connectivity, aviation data compliance, data scientists use Python/Jupyter.
QUESTION:
How should you design the ingestion pipeline to handle the intermittent connectivity and high data volume from the aircraft engines?
GCP PCA · Question 14 · Security Design
CASE STUDY: AeroMech
Overview: Aviation manufacturer, 5000 employees, $2B revenue. 100 engines, 10k sensors/engine, 1GB data/flight. On-prem Hadoop.
Business Req: Predictive maintenance, secure data sharing with airlines, monetize data.
Execs: CEO wants new revenue; CFO demands ML ROI; CTO says on-prem storage unfeasible.
Tech Req: High-throughput ingestion, PB-scale storage, train ML on historical data, deploy ML to edge (aircraft).
Constraints: Intermittent low-bandwidth flight connectivity, aviation data compliance, data scientists use Python/Jupyter.
QUESTION:
How should you securely share engine performance data with airline customers to create new revenue streams?
Answer options:
Export data to CSV files and host them on an external FTP server.
Create IAM users in AeroMech's GCP organization for every airline employee.
Use BigQuery Analytics Hub to securely share datasets with the airlines' GCP projects.
Set up VPC Peering between AeroMech and all airline networks.
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