We are recently working with Amazon MSK at Fortis, where we have multiple dashboards in our revenue intelligence platform. We are streaming data from different apps into those dashboards. The data flow goes from applications through Spark and then to our dashboard. We are using Amazon MSK for changes in the CDC pipelines, in the DBs, and for logging. We use it for every microservice communication that we are building for the product. We decouple the services with async event streaming at Amazon MSK. We have used Amazon MSK for IoT data streaming because in our product, we collect audio from retail shop floors. We have an IoT device on stream and a Raspberry Pi on stream. We can set up clouds on tracking devices and track everything from there through Amazon MSK, including their Docker health. It streams through Amazon MSK into our common database.
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.
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.
Senior Software Engineer at a recreational facilities/services company with 1,001-5,000 employees
Real User
Top 10
Jun 12, 2024
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.
Integration Solution Architect at a consultancy with 11-50 employees
Real User
Top 5
May 24, 2024
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.
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.
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.
We are recently working with Amazon MSK at Fortis, where we have multiple dashboards in our revenue intelligence platform. We are streaming data from different apps into those dashboards. The data flow goes from applications through Spark and then to our dashboard. We are using Amazon MSK for changes in the CDC pipelines, in the DBs, and for logging. We use it for every microservice communication that we are building for the product. We decouple the services with async event streaming at Amazon MSK. We have used Amazon MSK for IoT data streaming because in our product, we collect audio from retail shop floors. We have an IoT device on stream and a Raspberry Pi on stream. We can set up clouds on tracking devices and track everything from there through Amazon MSK, including their Docker health. It streams through Amazon MSK into our common database.
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.
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.
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.
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.
We use the software to facilitate building integrations between systems.
We are currently running tests and experimenting with other solutions.
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.
We are only using Amazon MSK for basic use cases.
Behind the scenes, we use Kafka. We tried using MSK to collect old data surrounding inventory from e-commerce websites, for example.