How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?
Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
Explanations:
Amazon MSK is a fully managed Kafka service, but Kinesis Data Analytics is better suited for real-time stream processing in this scenario.
Storing data in S3 using Kinesis Data Firehose introduces unnecessary complexity, as S3 is not ideal for real-time processing.
Kinesis Data Streams is an ideal solution for event ingestion. Kinesis Data Analytics can easily process the latest 10 minutes of data.
Using MSK with AWS Lambda would introduce unnecessary complexity, and Lambda is not optimized for handling real-time event streams at scale.
I classify that the answer is:
Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
I rate that the answer is:
Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.