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CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars.
Current Technical Environment:
- 1 million connected cars currently on the road
- Cars send telemetry data (speed, engine temp, location) every 5 seconds
- Current on-premises MQTT brokers are crashing under the load
Business Requirements:
- Enable predictive maintenance to alert drivers before parts fail
- Provide real-time fleet tracking for commercial customers
- Support over-the-air (OTA) software updates
Executive Statements:
- CEO: "Data is our new revenue stream. We need to monetize this telemetry data."
- CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention."
- CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity."
Technical Requirements:
- Ingest up to 100,000 messages per second
- Low-latency processing for real-time alerts
- Time-series data storage for historical analysis
- Handle variable network connectivity (cars driving through tunnels)
Constraints:
- Strict budget for data ingestion
- Small data engineering team
QUESTION:
To meet the CTO's requirement for infinite scaling and the technical requirement to ingest 100,000 messages per second, which ingestion and processing pipeline should you design?
GCP PCA · Question 17 · Domain 2: Managing and Provisioning a Solution Infrastructure
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars.
Current Technical Environment:
- 1 million connected cars currently on the road
- Cars send telemetry data (speed, engine temp, location) every 5 seconds
- Current on-premises MQTT brokers are crashing under the load
Business Requirements:
- Enable predictive maintenance to alert drivers before parts fail
- Provide real-time fleet tracking for commercial customers
- Support over-the-air (OTA) software updates
Executive Statements:
- CEO: "Data is our new revenue stream. We need to monetize this telemetry data."
- CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention."
- CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity."
Technical Requirements:
- Ingest up to 100,000 messages per second
- Low-latency processing for real-time alerts
- Time-series data storage for historical analysis
- Handle variable network connectivity (cars driving through tunnels)
Constraints:
- Strict budget for data ingestion
- Small data engineering team
QUESTION:
When designing the Cloud Bigtable schema for the telemetry data, how should you structure the row key to prevent hotspotting and allow efficient querying of a specific car's history?
CASE STUDY: AutoMakers Inc
Company Overview:
AutoMakers Inc is a global vehicle manufacturer. They have recently launched a line of connected cars.
Current Technical Environment:
- 1 million connected cars currently on the road
- Cars send telemetry data (speed, engine temp, location) every 5 seconds
- Current on-premises MQTT brokers are crashing under the load
Business Requirements:
- Enable predictive maintenance to alert drivers before parts fail
- Provide real-time fleet tracking for commercial customers
- Support over-the-air (OTA) software updates
Executive Statements:
- CEO: "Data is our new revenue stream. We need to monetize this telemetry data."
- CTO: "We expect to have 10 million connected cars in 3 years. The architecture must scale infinitely without manual intervention."
- CFO: "The cost of ingesting and storing this data must be strictly controlled. We cannot pay for idle capacity."
Technical Requirements:
- Ingest up to 100,000 messages per second
- Low-latency processing for real-time alerts
- Time-series data storage for historical analysis
- Handle variable network connectivity (cars driving through tunnels)
Constraints:
- Strict budget for data ingestion
- Small data engineering team
QUESTION:
When designing the Cloud Bigtable schema for the telemetry data, how should you structure the row key to prevent hotspotting and allow efficient querying of a specific car's history?
Answer options:
Use a row key format of [timestamp]#[car_id].
Use a row key format of [car_id]#[reversed_timestamp].
Use a completely random UUID for the row key.
Use an auto-incrementing integer as the row key.
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