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.
"Great auto-scaling, auto-sharing, and auto-correction features."
"Amazon Kinesis also provides us with plenty of flexibility."
"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."
"The solution works well in rather sizable environments."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"The scalability is pretty good."
"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 is user-friendly and the reporting is good."
"Allows us to process batch data, stream to real-time and build pipelines."
"The setup was not too difficult."
"Easy to deploy and manage."
"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's best feature is its data streaming tool."
"The documentation is very good."
"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."
"We were charged high costs for the solution’s enhanced fan-out feature."
"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."
"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."
"Could include features that make it easier to scale."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"In order to do a successful setup, the person handling the implementation needs to know the solution very well. You can't just come into it blind and with little to no experience."
"There is a learning curve. It takes time to learn."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"Apache Flink should improve its data capability and data migration."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"In a future release, they could improve on making the error descriptions more clear."
"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."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
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.
