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Answer: Employ AWS IoT Core to collect vehicle data. Set up rules to direct the data to an Amazon Kinesis Data Firehose delivery stream for storage in Amazon S3. Establish an Amazon Kinesis Data Analytics application to read from the delivery stream for anomaly detection.
The correct answer is B. Using AWS IoT Core to receive the vehicle data offers a managed service specifically designed to handle device communication via MQTT, which matches the company's current protocol. Configuring rules to route the data to an Amazon Kinesis Data Firehose delivery stream ensures scalable, real-time data processing and storage in Amazon S3 with minimal operational overhead. Amazon Kinesis Data Analytics allows for real-time data analysis, enabling the company to detect anomalies effectively. Option B provides an optimized solution with lower operational overhead compared to other options that involve more complex integrations or additional on-premises changes.
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A company that manufactures smart vehicles utilizes a custom application to gather data from these vehicles. The vehicles communicate with the application using the MQTT protocol, and the data is processed in 5-minute intervals. Subsequently, the vehicle telematics data is transferred to an on-premises storage system. Custom applications are employed to analyze this data for anomaly detection. However, the increasing number of vehicles and the higher data volumes generated by newer models have rendered the on-premises storage solution incapable of scaling to meet peak traffic demands, leading to data loss. To address these scaling issues, the company plans to modernize and migrate the solution to AWS. What AWS-based solution offers the least operational overhead to meet these requirements?
A
Utilize AWS IoT Greengrass to transmit vehicle data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Develop an Apache Kafka application to store the data in Amazon S3. Employ a pre-trained model in Amazon SageMaker for anomaly detection.
B
Employ AWS IoT Core to collect vehicle data. Set up rules to direct the data to an Amazon Kinesis Data Firehose delivery stream for storage in Amazon S3. Establish an Amazon Kinesis Data Analytics application to read from the delivery stream for anomaly detection.
C
Implement AWS IoT FleetWise to gather vehicle data. Forward the data to an Amazon Kinesis data stream. Utilize an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Leverage the built-in machine learning transforms in AWS Glue for anomaly detection.
D
Deploy Amazon MQ for RabbitMQ to collect vehicle data. Direct the data to an Amazon Kinesis Data Firehose delivery stream for storage in Amazon S3. Utilize Amazon Lookout for Metrics for anomaly detection.