We performed a comparison between Amazon Kinesis and Apache Flink based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Based on the parameters we compared, users are happier with Amazon Kinesis. Although it is not open-source like Apache Flink, Amazon Kinesis users were more satisfied with how the product performed, Apache Flink users were less satisfied with the overall functionality of the product, including its lack of stability and scalability.
"Everything is hosted and simple."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"I have worked in companies that build tools in-house. They face scaling challenges."
"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"The most valuable feature is that it has a pretty robust way of capturing things."
"The solution's technical support is flawless."
"One of the best features of Amazon Kinesis is the multi-partition."
"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."
"Apache Flink's best feature is its data streaming tool."
"Easy to deploy and manage."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"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."
"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."
"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."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"I think the default settings are far too low."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"Amazon Kinesis should improve its limits."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"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."
"There is room for improvement in the initial setup process."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"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."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"The solution could be more user-friendly."
"The machine learning library is not very flexible."
Amazon Kinesis is ranked 1st in Streaming Analytics with 24 reviews while Apache Flink is ranked 5th in Streaming Analytics with 15 reviews. Amazon Kinesis is rated 8.0, while Apache Flink is rated 7.6. The top reviewer of Amazon Kinesis writes "Used for media streaming and live-streaming data". 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 Kinesis is most compared with Azure Stream Analytics, Amazon MSK, Confluent, Google Cloud Dataflow and Apache Spark Streaming, whereas Apache Flink is most compared with Spring Cloud Data Flow, Databricks, Azure Stream Analytics, Apache Pulsar and Google Cloud Dataflow. See our Amazon Kinesis vs. Apache Flink report.
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We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.