Which combination of steps should a solutions architect take to meet these requirements?
(Choose two.)
Use AWS Glue to process the raw data in Amazon S3.
Use Amazon Route 53 to route traffic to different EC2 instances.
Add more EC2 instances to accommodate the increasing amount of incoming data.
Send the raw data to Amazon Simple Queue Service (Amazon SQS). Use EC2 instances to process the data.
Use Amazon API Gateway to send the raw data to an Amazon Kinesis data stream. Configure Amazon Kinesis Data Firehose to use the data stream as a source to deliver the data to Amazon S3.
Explanations:
AWS Glue can be used to process and transform data stored in S3, providing a serverless ETL (extract, transform, load) solution that scales automatically as data volume increases, minimizing operational overhead.
Amazon Route 53 is a DNS service that routes traffic but does not inherently scale applications or handle increased loads from millions of devices effectively. It’s not a suitable solution for the data ingestion challenge.
Adding more EC2 instances can accommodate increased data but requires manual scaling and management, leading to higher operational overhead and complexity as device numbers grow into millions.
While using Amazon SQS can help with decoupling and buffering, relying on EC2 instances to process data can lead to scalability and operational challenges, particularly with millions of devices generating data simultaneously.
Using Amazon API Gateway with Kinesis provides a scalable solution for ingesting real-time data. Kinesis can handle high throughput from millions of devices, and Kinesis Data Firehose simplifies delivery to S3, reducing operational overhead.
I scheme that the answer is:
Use AWS Glue to process the raw data in Amazon S3.
I reckon the answer is:
Use AWS Glue to process the raw data in Amazon S3.