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.
"I have worked in companies that build tools in-house. They face scaling challenges."
"The scalability is pretty good."
"The management and analytics are valuable features."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."
"The most valuable feature is that it has a pretty robust way of capturing things."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The solution's technical support is flawless."
"The setup was not too difficult."
"Easy to deploy and manage."
"The documentation is very good."
"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."
"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 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."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"Allows us to process batch data, stream to real-time and build pipelines."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Lacks first in, first out queuing."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"The price is not much cheaper. So, there is room for improvement in the pricing."
"Could include features that make it easier to scale."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"In a future release, they could improve on making the error descriptions more clear."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"The machine learning library is not very flexible."
"Apache Flink's documentation should be available in more languages."
"There is room for improvement in the initial setup process."
"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."
"There is a learning curve. It takes time to learn."
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.
See our list of best Streaming Analytics vendors.
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.