We have recently started using Amazon SQS and we are in the R&D phase. We want to see how resilient the solution is. We use Amazon SQS for integration purposes between our different applications.

The tool I use to transform and move data can read the entries from Amazon SQS. For example, to start some workflow orchestration, it checks Amazon SQS, reads new messages from it, and then runs some transformation. My responsibility was setting up the new SQS, setting up the right policies, adding some text, and allowing communication.

Amazon SQS is basically a queue service. Each message is treated as an event and added to a container. When a message needs to be processed, a trigger can be set.

Generally, we use it for asynchronous communication. We have actively utilized it for the past three years. Basically, we use it to exchange events and messages when we need communication and integration in our architecture.

It's a streaming application. For example, it is basically an autonomous vehicle solution for the case management side. It gives alerts and we have devices that will connect to IoT and the other service processes to provide data and then put it in a queue. 

We use the service in cloud-native event-driven integrations.

The primary use case of this solution is to send and receive messages across applications.

This is an event-driven solution so messages go through the API gateway onto the queue and are then processed from there. We are customers of Amazon.

As an IT company, we develop various solutions and products for our clients. This particular item is utilized as a component of a comprehensive solution for specific customer needs across various industries.

Amazon SQS is not a complex solution, we mainly use it for its primary purpose. It's an integral part of our application's workflow.

The primary use case of this solution is for collecting event data for analytic purposes.

In our case, when a user selects a function in our app, the information is collected. Analytics is performed on the data to determine the functionalities that clients use the most. 

Additionally, the data can tell us the most popular service demands such that resources can be assigned accordingly.