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A food monitoring agency has deployed over 10,000 water-level monitoring sensors that continuously send data updates, each under 1 MB. These updates are processed by on-premises application servers, which convert the raw data into a readable format and store it in an on-premises relational database. Data analysts use SQL queries to monitor this data. The agency seeks to enhance application availability and minimize maintenance efforts, which currently cause downtime and data loss when servers are unavailable. They aim to optimize operational overhead and costs. A solutions architect suggests using AWS IoT Core for data collection. What additional recommendations should the solutions architect make to meet these requirements?
A
Direct sensor data to Amazon Kinesis Data Firehose, then use an AWS Lambda function to convert the data to .csv format and insert it into an Amazon Aurora MySQL DB instance. Advise data analysts to query the data directly from this DB instance.
B
Route sensor data to Amazon Kinesis Data Firehose, then use an AWS Lambda function to convert the data to Apache Parquet format and store it in an Amazon S3 bucket. Instruct data analysts to query the data using Amazon Athena.
C
Send sensor data to an Amazon Managed Service for Apache Flink application to convert it to .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance for analysts to query directly.
D
Forward sensor data to an Amazon Managed Service for Apache Flink application to convert it to Apache Parquet format and store it in an Amazon S3 bucket. Guide data analysts to query the data using Amazon Athena.