Amazon MSK vs Apache Flink comparison

"Amazon MSK has significantly improved our organization by building seamless integration between systems."

"Amazon MSK's scalability is very good."

"Amazon MSK's separation of concerns and ease of creating and deploying new features are highly valuable. It just requires to assign them to the topic, and then anyone who needs to consume these messages can do so directly from Amazon MSK. This separation of concerns makes it very convenient, especially for new feature development, as developers can easily access the messages they need without having to deal with complex server communications or protocol setups."

"The solution's technical support was helpful."

"It offers good stability."

"It is a stable product."

"Amazon MSK has good integration because our team has been undergoing significant changes. Coupling it with MSK within AWS is helpful. We don't have to set up additionals or monitor external environments. This"

"Overall, it is very cost-effective based on the workflow."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."

"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."

"Allows us to process batch data, stream to real-time and build pipelines."

"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."

"The documentation is very good."

"Easy to deploy and manage."

"This is truly a real-time solution."

"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."

"In my opinion, there are areas in Amazon MSK that could be improved, particularly in terms of configuration. Initially setting it up and getting it connected was quite challenging. The naming conventions for policies were updated by AWS, and some were undocumented, leading to confusion with outdated materials. It took us weeks of trial and error before discovering new methods through hidden tutorials and official documentation."

"The configuration seems a little complex and the documentation on the product is not available."

"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."

"One of the reasons why we prefer Kafka is because the support is a little bit difficult to manage with Amazon MSK."

"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."

"Horizontal scale-out is actually not easy, making it an area where improvements are required."

"It should be more flexible, integration-wise."

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

"The solution could be more user-friendly."

"In a future release, they could improve on making the error descriptions more clear."

"There are more libraries that are missing and also maybe more capabilities for machine learning."

"The machine learning library is not very flexible."

"There is room for improvement in the initial setup process."

"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."

"The platform has better pricing than one of its competitors."

"The price of Amazon MSK is less than some competitor solutions, such as Confluence."

"When you create a complete enterprise-driven architecture that is deployable on an enterprise scale, I would say that the prices of Amazon MSK and Confluent Platform become comparable."

"This is an open-source platform that can be used free of charge."

"It's an open source."

"The solution is open-source, which is free."

"It's an open-source solution."

"Apache Flink is open source so we pay no licensing for the use of the software."