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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 12 · Storage Systems
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:
To manage the PB-scale storage of historical flight data cost-effectively, what should you implement?
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:
To manage the PB-scale storage of historical flight data cost-effectively, what should you implement?
Answer options:
Store all data in BigQuery active storage to ensure it is always ready for ML training.
Store data in Cloud Storage and use Object Lifecycle Management to transition older data to Coldline or Archive classes.
Provision a massive Persistent Disk (PD-Standard) and attach it to a Compute Engine instance.
Use Cloud Filestore to provide an NFS mount for the data scientists.
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