Amazon Kinesis vs Apache Flink comparison

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #2 with an average rating of 7.9, while Apache is ranked #5 with an average rating of 7.6. Amazon Web Services (AWS) holds a 9.1% mindshare in SA, compared to Apache’s 12.6% mindshare. Additionally, 89% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 94% of Apache users who would recommend it.

Amazon Kinesis

Read 27 Amazon Kinesis reviews

6,323 Views
4,745 Comparison Views

89% willing to recommend

Apache Flink

Read 16 Apache Flink reviews

5,503 Views
3,955 Comparison Views

94% willing to recommend

Amazon Kinesis

Apache Flink

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 12, 2025

Apache Flink and Amazon Kinesis are leading products in the data streaming category. Apache Flink stands out for its real-time processing capabilities and flexibility, while Amazon Kinesis has an advantage due to its seamless integration with AWS services, making it easier for users within the AWS ecosystem.

Features: Apache Flink provides advanced event processing, high throughput, and sophisticated streaming applications. It excels in handling complex analytics tasks and supports stateful transformations, maintaining message order without the need for external caches. Amazon Kinesis offers robust scalability, integration capabilities, and ease of use due to its managed nature. With features like auto-scaling and real-time data processing, Kinesis simplifies data handling within AWS.

Room for Improvement: Apache Flink's deployment complexity and high initial setup time are potential drawbacks. Its reliance on high RAM and memory requirements for stateful computations can be demanding. Amazon Kinesis could improve in areas such as handling data volume efficiently and maintaining processing speed during peak loads. Additionally, enhancing its documentation for more complex integrations would benefit users seeking advanced solutions.

Ease of Deployment and Customer Service: Apache Flink may require substantial effort for deployment, needing extensive setup and configuration. Customization and tuning can lead to longer deployment times. In contrast, Amazon Kinesis benefits from its managed setup, which simplifies deployment for AWS users. The extensive support resources provided by AWS further streamline the deployment process, although some users may experience initial challenges adapting to new concepts.

Pricing and ROI: Apache Flink allows for flexible setup in self-managed environments, potentially offering lower costs and better ROI by leveraging full capabilities. Initial higher setup costs can be offset through customization in complex applications. Amazon Kinesis provides a scalable, pay-as-you-go pricing model, which helps manage costs effectively, especially for varying workloads. The managed service model allows businesses to control expenses while benefiting from tight integration with the AWS ecosystem.

To learn more, read our detailed Amazon Kinesis vs. Apache Flink Report (Updated: March 2025).

Buyer's Guide

Amazon Kinesis vs. Apache Flink

March 2025

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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

Ranking in other categories

No ranking in other categories

Apache Flink

Ranking in Streaming Analytics

5th

Average Rating

7.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.1%, down from 14.2% compared to the previous year. The mindshare of Apache Flink is 12.6%, up from 9.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

Used for media streaming and live-streaming data

It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.

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Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

A great solution with an intricate system and allows for batch data processing

We value this solution's intricate system because it comes with a state inside the mechanism and product. The system allows us to process batch data, stream to real-time and build pipelines. Additionally, we do not need to process data from the beginning when we pause, and we can continue from the same point where we stopped. It helps us save time as 95% of our pipelines will now be on Amazon, and we'll save money by saving time.

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Quotes from Members

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

Pros

"Amazon Kinesis also provides us with plenty of flexibility."

"I find almost all features valuable, especially the timing and fast pace movement."

"The scalability is pretty good."

"The Kinesis VideoStream and DataStream are the most important features."

"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."

"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 product's initial setup phase is not difficult because we are using the tool on the cloud."

"Everything is hosted and simple."

More Amazon Kinesis pros

"The setup was not too difficult."

"Apache Flink offers a range of powerful configurations and experiences for development teams. Its strength lies in its development experience and capabilities."

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

"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 documentation is very 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."

More Apache Flink pros

Cons

"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."

"The price is not much cheaper. So, there is room for improvement in the pricing."

"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."

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"We were charged high costs for the solution’s enhanced fan-out feature."

"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."

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

"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."

More Amazon Kinesis cons

"Apache Flink's documentation should be available in more languages."

"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."

"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."

"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."

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"There is room for improvement in the initial setup process."

More Apache Flink cons

Pricing and Cost Advice

"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."

"Amazon Kinesis is an expensive solution."

"The tool's pricing is cheap."

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

"The tool's entry price is cheap. However, pricing increases with data volume."

"The product falls on a bit of an expensive side."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"The solution's pricing is fair."

More Amazon Kinesis pricing and cost advice

"This is an open-source platform that can be used free of charge."

"Apache Flink is open source so we pay no licensing for the use of the software."

"The solution is open-source, which is free."

"It's an open-source solution."

"It's an open source."

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Top Industries

By visitors reading reviews

Computer Software Company

19%

Financial Services Firm

17%

Manufacturing Company

Retailer

Financial Services Firm

23%

Computer Software Company

16%

Manufacturing Company

Retailer

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.

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What is your experience regarding pricing and costs for Amazon Kinesis?

Amazon Kinesis is moderately priced. In comparison with other competitors, it is fairly priced, however, if they reduced the price a little, it could add more value to customers.

See all answers

What needs improvement with Amazon Kinesis?

I do not see any scope for improvement as it does what it is supposed to do. No changes are required. Since it's predominantly a back-end service, any end-user isn't going to interact with it direc...

See all answers

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. ...

See all answers

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.

See all answers

What needs improvement with Apache Flink?

There are more libraries that are missing and also maybe more capabilities for machine learning. It could have a friendly user interface for pipeline configuration, deployment, and monitoring.

See all answers

Comparisons

Confluent vs Amazon Kinesis

Compared 29% of the time

Azure Stream Analytics vs Amazon Kinesis

Compared 24% of the time

Amazon MSK vs Amazon Kinesis

Compared 11% of the time

Databricks vs Amazon Kinesis

Compared 5% of the time

Google Cloud Dataflow vs Amazon Kinesis

Compared 5% of the time

More Amazon Kinesis Competitors

Spring Cloud Data Flow vs Apache Flink

Compared 25% of the time

Databricks vs Apache Flink

Compared 21% of the time

Azure Stream Analytics vs Apache Flink

Compared 12% of the time

Google Cloud Dataflow vs Apache Flink

Compared 8% of the time

Amazon MSK vs Apache Flink

Compared 5% of the time

More Apache Flink Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

March 2025

Download Amazon Kinesis product report

Buyer's Guide

Apache Flink

March 2025

Download Apache Flink product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Flink

Overview

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Amazon Web Services (AWS)

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “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.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

Apache

Sample Customers

Zillow, Netflix, Sonos

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

Buyer's Guide

Amazon Kinesis vs. Apache Flink

March 2025

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