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Amazon Kinesis vs Apache Flink comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 20, 2025

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Apache Flink
Ranking in Streaming Analytics
5th
Average Rating
7.8
Reviews Sentiment
6.9
Number of Reviews
18
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.0%, down from 12.6% compared to the previous year. The mindshare of Apache Flink is 13.9%, up from 9.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Prabin Silwal - PeerSpot reviewer
Pipeline setup is very simple
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
Aswini Atibudhi - PeerSpot reviewer
Enables robust real-time data processing but documentation needs refinement
Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every ( /products/every-reviews ) software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging ( /categories/debugging ) and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The product's initial setup phase is not difficult because we are using the tool on the cloud."
"Everything is hosted and simple."
"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."
"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."
"Amazon Kinesis also provides us with plenty of flexibility."
"The most valuable feature is that it has a pretty robust way of capturing things."
"The integration capabilities of the product are good."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"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."
"Allows us to process batch data, stream to real-time and build pipelines."
"This is truly a real-time solution."
"Easy to deploy and manage."
"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."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"The documentation is very good."
"It is user-friendly and the reporting is good."
 

Cons

"The price is not much cheaper. So, there is room for improvement in the pricing."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."
"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."
"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."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"If there were better documentation on optimal sharding strategies then it would be helpful."
"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"There is a learning curve. It takes time to learn."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."
"There is room for improvement in the initial setup process."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing."
"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."
"Apache Flink's documentation should be available in more languages."
 

Pricing and Cost Advice

"The fee is based on the number of hours the service is running."
"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."
"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."
"The tool's pricing is cheap."
"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."
"The product falls on a bit of an expensive side."
"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."
"Amazon Kinesis is an expensive solution."
"Apache Flink is open source so we pay no licensing for the use of the software."
"It's an open-source solution."
"The solution is open-source, which is free."
"This is an open-source platform that can be used free of charge."
"It's an open source."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
17%
Manufacturing Company
10%
Educational Organization
4%
Financial Services Firm
24%
Computer Software Company
14%
Manufacturing Company
7%
Retailer
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
What do you like most about Apache Flink?
The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. ...
What is your experience regarding pricing and costs for Apache Flink?
The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.
What needs improvement with Apache Flink?
Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool. There is a need for increased awareness and education, especially around best ...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
Flink
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
Find out what your peers are saying about Amazon Kinesis vs. Apache Flink and other solutions. Updated: June 2025.
860,592 professionals have used our research since 2012.