A company has an application that ingests incoming messages. Dozens of other applications and microservices then quickly consume these messages. The number of messages varies drastically and sometimes increases suddenly to 100,000 each second. The company wants to decouple the solution and increase scalability.
Which solution meets these requirements?
The solution that best meets the company's requirements of ingesting and quickly consuming up to 100,000 messages each second while decoupling the solution and increasing scalability is to publish messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. This configuration provides a scalable and decoupled architecture as Amazon SNS can handle high message throughput and enables multiple consumer applications to process the messages in parallel through SQS. This approach ensures that the system can handle drastic variations in message volume efficiently.
D makes more sense to me.
of course, the answer is D
By default, an SQS queue can handle a maximum of 3,000 messages per second. However, you can request higher throughput by contacting AWS Support. AWS can increase the message throughput for your queue beyond the default limits in increments of 300 messages per second, up to a maximum of 10,000 messages per second. It's important to note that the maximum number of messages per second that a queue can handle is not the same as the maximum number of requests per second that the SQS API can handle. The SQS API is designed to handle a high volume of requests per second, so it can be used to send messages to your queue at a rate that exceeds the maximum message throughput of the queue.
The limit that you're mentioning apply to FIFO queues. Standard queues are unlimited in throughput (https://aws.amazon.com/sqs/features/). Do you think that the use case require FIFO queue ?
D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SQS) subscriptions. Configure the consumer applications to process the messages from the queues. This solution uses Amazon SNS and SQS to publish and subscribe to messages respectively, which decouples the system and enables scalability by allowing multiple consumer applications to process the messages in parallel. Additionally, using Amazon SQS with multiple subscriptions can provide increased resiliency by allowing multiple copies of the same message to be processed in parallel.
D. SNS Fan Out Pattern https://docs.aws.amazon.com/sns/latest/dg/sns-common-scenarios.html (A is wrong Kinesis Analysis does not 'persist' by itself.)
Decouple and increase scalability are clear use cases for SNS and SQS. D it is!
SNS and SQS combination is the only feasible option provided here.
In this case is D because the requirement is to decouple the solution, the only way is to use AWS SQS.
Decouple systems and increase scalability - SNS & SQS.
The SNS to SQS fanout method, is an appropriate solution for this. the SNS polls the messages from the application in batch and then the SQS queue will poll the massages from the SNS with FIFO or other method, to give it for the consumers. that is the best architecture.
D makes more sense to me.
D makes more sense to me.
D seems to be the correct answer
Using Amazon SNS and SQS together provides a scalable, decoupled architecture that can handle high message throughput and variable load efficiently, making it the best fit for the requirements.
Answer is A because you have to decouple the solution. Anything ecoupling has to do with kinesis
D for sure
pq a letra A é a correta? Pq a AWS da preferencia ao serviço que possui mais disponibilidade e escalabilidade: A escolha da opção A pode ser explicada pela robustez e escalabilidade do Amazon Kinesis Data Streams combinada com Kinesis Data Analytics. Esses serviços permitem: Ingestão de Alto Volume: Kinesis Data Streams pode facilmente escalar para acomodar picos de 100.000 mensagens por segundo. Desacoplamento Eficiente: Os shards de Kinesis permitem que múltiplos consumidores processem dados de forma desacoplada e paralela. Análise em Tempo Real: Kinesis Data Analytics possibilita transformar e analisar dados em tempo real, oferecendo valor imediato a partir das mensagens ingeridas.
SNS decouple the order and secure the message during transferring
D is accurate answer
SNS can handle the high throughput of up to 100,000 messages per second. Amazon Kinesis typical use cases are 1.Real-Time Dashboards 2.Streaming ETL (Extract, Transform, Load) 3.Log and Event Monitoring 4. IoT Analytics 5. Clickstream Analysis