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.
"The solution works well in rather sizable environments."
"Everything is hosted and simple."
"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 solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The scalability is pretty good."
"Amazon Kinesis also provides us with plenty of flexibility."
"One of the best features of Amazon Kinesis is the multi-partition."
"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."
"This is truly a real-time solution."
"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."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"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."
"It is user-friendly and the reporting is good."
"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."
"Easy to deploy and manage."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"Could include features that make it easier to scale."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"I think the default settings are far too low."
"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."
"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."
"Amazon Kinesis should improve its limits."
"There is a learning curve. It takes time to learn."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"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."
"The solution could be more user-friendly."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"The machine learning library is not very flexible."
"Apache Flink should improve its data capability and data migration."
Amazon Kinesis is ranked 2nd in Streaming Analytics with 21 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.
