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Amazon Kinesis vs Apache Flink comparison

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7,130 views|5,506 comparisons
Featured Review
Find out what your peers are saying about Amazon Kinesis vs. Apache Flink and other solutions. Updated: January 2022.
563,208 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
"The solution works well in rather sizable environments.""Amazon Kinesis has improved our ROI.""Great auto-scaling, auto-sharing, and auto-correction features.""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.""The scalability is pretty good.""Amazon Kinesis also provides us with plenty of flexibility.""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.""Everything is hosted and simple."

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"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.""The documentation is very good.""It is user-friendly and the reporting is good.""The setup was not too difficult.""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.""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.""This is truly a real-time solution.""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."

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"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.""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.""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.""Could include features that make it easier to scale.""Lacks first in, first out queuing.""I think the default settings are far too low."

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"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.""There is a learning curve. It takes time to learn.""In a future release, they could improve on making the error descriptions more clear.""The machine learning library is not very flexible.""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.""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 improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves.""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."

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Pricing and Cost Advice
  • "Under $1,000 per month."
  • "The solution's pricing is fair."
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  • "This is an open-source platform that can be used free of charge."
  • "The solution is open-source, which is free."
  • "Apache Flink is open source so we pay no licensing for the use of the software."
  • "It's an open-source solution."
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    Questions from the Community
    Top Answer: 
    The solution's pricing is fair. The trick lies in Amazon's pricing. They charge according to the different layers of or types of data that is transfered.
    Top Answer: 
    Amazon Kinesis is not a bad product, but Azure Event Hub provides us with certain operational advantages, as our focus is on Microsoft related coding. This is why .NET is what we use at the backend… more »
    Top Answer: 
    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… more »
    Top Answer: 
    Apache Flink is open source so we pay no licensing for the use of the software.
    Top Answer: 
    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… more »
    out of 38 in Streaming Analytics
    Average Words per Review
    out of 38 in Streaming Analytics
    Average Words per Review
    Also Known As
    Amazon AWS Kinesis, AWS Kinesis, Kinesis
    Learn More

    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.

    Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.

    Learn more about Amazon Kinesis
    Learn more about Apache Flink
    Sample Customers
    Zillow, Netflix, Sonos
    LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
    Top Industries
    Computer Software Company23%
    Media Company20%
    Comms Service Provider18%
    Financial Services Firm9%
    Computer Software Company26%
    Comms Service Provider20%
    Media Company11%
    Financial Services Firm10%
    Company Size
    Small Business50%
    Midsize Enterprise38%
    Large Enterprise13%
    Small Business22%
    Midsize Enterprise11%
    Large Enterprise67%
    Find out what your peers are saying about Amazon Kinesis vs. Apache Flink and other solutions. Updated: January 2022.
    563,208 professionals have used our research since 2012.

    Amazon Kinesis is ranked 2nd in Streaming Analytics with 10 reviews while Apache Flink is ranked 4th in Streaming Analytics with 9 reviews. Amazon Kinesis is rated 8.4, while Apache Flink is rated 7.6. The top reviewer of Amazon Kinesis writes "Easily replay your streaming data with this reliable solution". On the other hand, the top reviewer of Apache Flink writes "Scalable framework for stateful streaming aggregations". Amazon Kinesis is most compared with Apache Spark Streaming, Confluent, Amazon MSK, Azure Stream Analytics and Google Cloud Dataflow, whereas Apache Flink is most compared with Spring Cloud Data Flow, Azure Stream Analytics, Databricks, Google Cloud Dataflow and Apache Pulsar. See our Amazon Kinesis vs. Apache Flink report.

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