We used Amazon MSK to manage high-volume third-party data entering our system. It served as a buffer when our system was unable to consume data at high speeds in real-time. The data initially went to Kafka and was then consumed by multiple consumers. One consumer sent data to the master database, and others sent it to related applications for further processing.

We are currently using Amazon MSK to transfer data from our PostgreSQL database to our DynamoDB, acting as a mediator between those two databases for migration purposes.

Our data is in an on-premise PostgreSQL database, and we are moving it to DynamoDB in AWS using Amazon MSK.

I have used Confluent Cloud and Amazon MSK in my company. We are not using it for analytics and it is more for CDC processes, so we change the capture processes. It is used to extract data from a database and make it available in other parts of our systems or produce events that inform us of data updates.

I use the solution in my company for our CDC pipelines to asynchronously collect data from databases and systems.

We use Amazon MSK for communication and notification purposes, such as notifying users about new game releases and managing customer support interactions, thus handling large data volumes.

We use the software to facilitate building integrations between systems.

My use cases were primarily in the banking domain, where we tracked real-time transactions.

Apart from real-time transactions, we also had another project in the retail domain. Our client's name was McDonolad, and their focus was on package delivery. By "package," I mean the customized burgers created at their restaurant that needed to be delivered to various restaurant locations. When customers visit their restaurant, they would order their personalized burgers. Sometimes, customers would request specific ingredients that were not available in the pre-made burgers displayed at the counter. The restaurant faced the challenge of understanding why customers were not opting for ready-made burgers or patties. To address this issue, they aimed to enhance their product by offering customized burgers. These customized burgers had to be prepared according to the customer's specific preferences. To achieve this, they started taking orders from customers. 

However, it was difficult to determine the exact taste or requirements the customers were looking for. To overcome this, they decided to transfer all the customer's order data to the Kafka server for streaming. This allowed the restaurant's backend system to analyze the data and understand the customer's new tastes and requirements. By collecting this data, they were able to enhance their product and make necessary changes to improve customer satisfaction.

Behind the scenes, we use Kafka. We tried using MSK to collect old data surrounding inventory from e-commerce websites, for example.

We are only using Amazon MSK for basic use cases.

We are currently running tests and experimenting with other solutions.