Which solution will meet these requirements with the LEAST development time?
Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using the Random Cut Forest (RCF) trained model in Amazon SageMaker. Visualize the data in Amazon QuickSight.
Use Amazon Kinesis Data Streams to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.
Explanations:
Amazon Kinesis Data Firehose allows for easy streaming of data to Amazon S3 with minimal development time. QuickSight ML Insights provides an easy-to-use tool for detecting outliers like high-demand rental cars, and it integrates well with QuickSight for visualization.
Amazon Kinesis Data Streams requires more development effort to stream the data compared to Data Firehose. Also, using Amazon SageMaker with a Random Cut Forest (RCF) model introduces unnecessary complexity for the task of detecting outliers, which can be more easily done in QuickSight.
Similar to option B, using Amazon Kinesis Data Firehose is correct, but integrating the Random Cut Forest (RCF) model in SageMaker adds unnecessary complexity. QuickSight’s ML Insights is a simpler, out-of-the-box solution for detecting high-demand outliers.
Amazon Kinesis Data Streams, like in option B, requires more development work. Additionally, Amazon QuickSight ML Insights does not directly work with streaming data from Kinesis Data Streams, so this solution would require more integration work and customization.
I surmise that the answer is:
Use Amazon Kinesis Data Firehose to stream the reservation data directly to Amazon S3. Detect high-demand outliers by using Amazon QuickSight ML Insights. Visualize the data in QuickSight.