What else should the solutions architect recommend to meet these requirements?
Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to .csv format, and insert it into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
Send the sensor data to Amazon Kinesis Data Firehose. Use an AWS Lambda function to read the Kinesis Data Firehose data, convert it to Apache Parquet format, and save it to an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.
Send the sensor data to an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to convert the data to .csv format and store it in an Amazon S3 bucket. Import the data into an Amazon Aurora MySQL DB instance. Instruct the data analysts to query the data directly from the DB instance.
Send the sensor data to an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to convert the data to Apache Parquet format and store it in an Amazon S3 bucket. Instruct the data analysts to query the data by using Amazon Athena.
Explanations:
While this option sends data to Kinesis Data Firehose and converts it to CSV format for insertion into an Amazon Aurora MySQL DB instance, it still requires the analysts to query the database directly. This does not optimize operational overhead as the agency still has to manage database updates and maintenance, which can cause downtime and lost data.
This option optimally sends sensor data to Kinesis Data Firehose, converts it to Apache Parquet format, and saves it to Amazon S3. By using Amazon Athena, data analysts can query the data directly from S3 without needing a dedicated database server, which reduces operational overhead and allows for higher availability without downtime.
Although this option utilizes Amazon Managed Service for Apache Flink to process the data, it ultimately stores the data in an Amazon Aurora MySQL DB instance. This requires ongoing maintenance of the database, leading to potential downtime and lost data. Additionally, querying directly from a database does not scale as effectively as querying from S3.
This option correctly uses Apache Flink to convert data to Apache Parquet format and store it in S3, which is good for analytics. However, it does not mention the use of Athena for querying, which is a key aspect of optimizing data access without incurring the overhead of managing a database. Thus, while close, it does not meet the full requirements as efficiently as option B.