Which solution will meet these requirements?
Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon. S3 Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis.
Migrate the application server to AWS Fargate, which will receive the information from IoT sensors and parse the information into a relational format. Save the parsed information to Amazon Redshlft for analysis.
Create an AWS Transfer for SFTP server. Update the IoT sensor code to send the information as a .csv file through SFTP to the server. Use AWS Glue to catalog the files. Use Amazon Athena for analysis.
Use AWS Snowball Edge to collect data from the IoT sensors directly to perform local analysis. Periodically collect the data into Amazon Redshift to perform global analysis.
Explanations:
This option efficiently utilizes AWS IoT Core to handle incoming sensor data, which can be parsed in real-time using AWS Lambda. Storing the data in .csv format on Amazon S3 allows for cost-effective storage and easy retrieval. AWS Glue can catalog the files, enabling seamless integration with Amazon Athena for ad-hoc querying and analysis, and QuickSight can be used for visualization, thus optimizing both performance and cost.
Migrating the application server to AWS Fargate does allow for scalable processing, but it requires the application to be rewritten for the Fargate environment. The reliance on Redshift for analysis can lead to higher costs compared to S3 and Athena, especially for a scenario requiring daily processing. This option does not optimize costs as effectively as option A.
While using AWS Transfer for SFTP is feasible, it requires modifications to the IoT sensor code to send data as .csv files. This introduces complexity and potential issues with data integrity and format consistency. Additionally, the solution would be slower due to reliance on file transfers rather than real-time data processing. Thus, it may not meet the requirement for faster analysis.
AWS Snowball Edge is designed for data transfer and local processing, which is not optimal for real-time analysis. It is more suited for scenarios where bandwidth is limited or for transferring large amounts of data to AWS. This solution adds unnecessary complexity and delay in data availability for global analysis, making it less effective for immediate data insights compared to the cloud-based options.
I scheme that the answer is:
Connect the IoT sensors to AWS IoT Core. Set a rule to invoke an AWS Lambda function to parse the information and save a .csv file to Amazon. S3 Use AWS Glue to catalog the files. Use Amazon Athena and Amazon QuickSight for analysis.