We performed a comparison between Amazon MSK and Apache Flink based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."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"
"MSK has a private network that's an out-of-box feature."
"It offers good stability."
"Overall, it is very cost-effective based on the workflow."
"The most valuable feature of Amazon MSK is the integration."
"Amazon MSK has significantly improved our organization by building seamless integration between systems."
"It is a stable product."
"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."
"Apache Flink's best feature is its data streaming tool."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"This is truly a real-time solution."
"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."
"Easy to deploy and manage."
"It is user-friendly and the reporting is good."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"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."
"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."
"It should be more flexible, integration-wise."
"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."
"The configuration seems a little complex and the documentation on the product is not available."
"Amazon MSK could improve on the features they offer. They are still lagging behind Confluence."
"Apache Flink's documentation should be available in more languages."
"In a future release, they could improve on making the error descriptions more clear."
"There is a learning curve. It takes time to learn."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"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."
"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
Amazon MSK is ranked 6th in Streaming Analytics with 7 reviews while Apache Flink is ranked 5th in Streaming Analytics with 15 reviews. Amazon MSK is rated 7.2, while Apache Flink is rated 7.6. The top reviewer of Amazon MSK writes "Streamlines our processes, and we don't need to configure any VPCs; it's automatic". On the other hand, the top reviewer of Apache Flink writes "A great solution with an intricate system and allows for batch data processing". Amazon MSK is most compared with Confluent, Azure Stream Analytics, Amazon Kinesis, Google Cloud Dataflow and Aiven for Apache Kafka, whereas Apache Flink is most compared with Spring Cloud Data Flow, Amazon Kinesis, Databricks, Azure Stream Analytics and Apache Spark Streaming. See our Amazon MSK vs. Apache Flink report.
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