Which solution will meet these requirements with the LEAST operational overhead?
Use AWS IoT Greengrass to send the vehicle data to Amazon Managed Streaming for Apache Kafka (Amazon MSK). Create an Apache Kafka application to store the data in Amazon S3. Use a pretrained model in Amazon SageMaker to detect anomalies.
Use AWS IoT Core to receive the vehicle data. Configure rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3. Create an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies.
Use AWS IoT FleetWise to collect the vehicle data. Send the data to an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use the built-in machine learning transforms in AWS Glue to detect anomalies.
Use Amazon MQ for RabbitMQ to collect the vehicle data. Send the data to an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Use Amazon Lookout for Metrics to detect anomalies.
Explanations:
While AWS IoT Greengrass and Apache Kafka can handle high data volumes, the operational overhead of managing Kafka and the complexity of integrating it with SageMaker for anomaly detection is higher compared to the other options. This may not meet the requirement of the least operational overhead.
AWS IoT Core simplifies the data ingestion process from vehicles, while Kinesis Data Firehose provides a seamless way to store the data in Amazon S3. Kinesis Data Analytics can easily analyze the data for anomalies with minimal management overhead, making it the best fit for scaling and operational efficiency.
Although AWS IoT FleetWise can collect vehicle data effectively, the integration with Kinesis and AWS Glue introduces additional complexity. Using Glue for anomaly detection is less direct and involves more operational overhead compared to the streamlined Kinesis Data Analytics in Option B.
Amazon MQ for RabbitMQ adds unnecessary complexity for vehicle data collection. While Kinesis Data Firehose is efficient for storage, using Amazon Lookout for Metrics for anomaly detection may not be as straightforward or integrated as using Kinesis Data Analytics. This option also doesn’t focus on minimizing operational overhead.
I design that the answer is:
Use AWS IoT Core to receive the vehicle data. Configure rules to route data to an Amazon Kinesis Data Firehose delivery stream that stores the data in Amazon S3. Create an Amazon Kinesis Data Analytics application that reads from the delivery stream to detect anomalies.