Amazon MSK vs Apache Flink comparison

"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."

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

"The scalability and usability are quite remarkable."

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

"It offers good stability."

"It is a stable product."

"The setup was not too difficult."

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."

"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."

"Easy to deploy and manage."

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

"Apache Flink's best feature is its data streaming tool."

"The ease of usage, even for complex tasks, stands out."

"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."

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

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

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

"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."

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

"The cost of using Amazon MSK is high, which is a significant disadvantage, as the increase in cloud costs by 50% to 60% does not justify the savings."

"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 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."

"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."

"Apache Flink should improve its data capability and data migration."

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

"The machine learning library is not very flexible."

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

"Apache Flink's documentation should be available in more languages."

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

"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."

"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."

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

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

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

"It's an open source."

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

"It's an open-source solution."

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