The company wants to integrate new sensors in a loosely-coupled, fully managed, and serverless way. The company cannot modify the application code.
Which solution will meet these requirements?
A.
Forward the existing stream in Kinesis Data Streams to Amazon Managed Service for Apache Flink for anomaly detection. Use a second stream in Kinesis Data Streams to send the Flink output to the application.
B.
Use Amazon Data Firehose to stream data to Amazon S3. Use Amazon Redshift Spectrum to perform anomaly detection on the S3 data. Use S3 Event Notifications to invoke an AWS Lambda function that sends analyzed data to the application through a second stream in Kinesis Data Streams.
C.
Configure Amazon EC2 instances in an Auto Scaling group to consume data from the data stream and to perform anomaly detection. Create a second stream in Kinesis Data Streams to send data from the EC2 instances to the application.
D.
Configure an Amazon Elastic Container Service (Amazon ECS) task that uses Amazon EC2 instances to consume data from the data stream and to perform anomaly detection. Create a second stream in Kinesis Data Streams to send data from the containers to the application.
Amazon Managed Service for Apache Flink (formerly Kinesis Data Analytics) is a fully managed, serverless service for real-time processing of streaming data. You can consume data from Kinesis Data Streams, perform anomaly detection, and then output the results to another Kinesis stream. This approach is loosely coupled, fully managed, and does not require modifying the application code.
AWS Documentation Extract:
“Amazon Managed Service for Apache Flink enables you to process streaming data in real time, integrating with Kinesis Data Streams as source and sink, and is fully managed and serverless.”
(Source: Apache Flink on AWS documentation)
B: S3/Redshift is not real-time and adds complexity.
C, D: EC2/ECS solutions are not serverless or fully managed.
[Reference: AWS Certified Solutions Architect – Official Study Guide, Real-Time Analytics., , ]
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