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CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
Which architecture should you recommend for the real-time ingestion and processing pipeline to meet the CTO's requirement for a fully managed serverless solution?
GCP PCA · Question 14 · Cost Optimization
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
To provide a scalable data warehouse for business intelligence analysts, how should you configure BigQuery to optimize query performance and costs for time-series telemetry data?
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a leading vehicle manufacturer transitioning to connected and autonomous vehicles. They need a platform to ingest, process, and analyze telemetry data from millions of cars.
Current Technical Environment:
- Legacy MQTT brokers on-premises.
- Hadoop cluster for batch processing (nightly runs).
- 100,000 connected cars sending 1 KB of data every minute.
- On-premises data warehouse reaching capacity.
Business Requirements:
- Support 5 million connected cars within 3 years.
- Enable real-time alerting for critical vehicle faults.
- Provide predictive maintenance insights to customers.
- Monetize anonymized traffic data.
Executive Statements:
- CEO: "Data is our new engine. We need real-time insights to improve safety."
- CFO: "The platform must scale cost-effectively. We only want to pay for what we use."
- CTO: "We need a fully managed serverless data pipeline to minimize operational overhead."
Technical Requirements:
- Ingest up to 1 million messages per second with low latency.
- Process data in real-time for anomaly detection.
- Store raw telemetry data indefinitely for machine learning model training.
- Provide a scalable data warehouse for business intelligence analysts.
Constraints:
- Strict data privacy regulations (GDPR) require masking of PII.
- Limited data engineering staff; prefer managed services.
- Must integrate with existing on-premises identity provider (Active Directory).
QUESTION:
To provide a scalable data warehouse for business intelligence analysts, how should you configure BigQuery to optimize query performance and costs for time-series telemetry data?
Answer options:
Create a new BigQuery table for every day of telemetry data (sharding).
Partition the BigQuery tables by a timestamp column and cluster by vehicle ID.
Cluster the BigQuery tables by timestamp and partition by vehicle ID.
Use BigQuery BI Engine and load all historical telemetry data into memory.
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