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Amazon MSK vs Apache Flink comparison

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6,998 views|6,043 comparisons
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Streaming Analytics
July 2022
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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
"MSK has a private network that's an out-of-box feature."

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

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Cons
"It should be more flexible, integration-wise."

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"In a future release, they could improve on making the error descriptions more clear.""We have a machine learning team that works with Python, but Apache Flink does not have full support for the language.""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.""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.""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.""There is a learning curve. It takes time to learn.""The machine learning library is not very flexible.""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
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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:MSK has a private network that's an out-of-box feature.
    Top Answer:MSK should be easier to integrate with other solutions. It should be more flexible, integration-wise. MSK has a private network that's an out-of-the-box feature. For our case, we needed something… more »
    Top Answer:Behind the scenes, we use Kafka. We tried using MSK to collect old data surrounding inventory from e-commerce websites, for example.
    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 »
    Ranking
    8th
    out of 38 in Streaming Analytics
    Views
    6,998
    Comparisons
    6,043
    Reviews
    1
    Average Words per Review
    410
    Rating
    8.0
    5th
    out of 38 in Streaming Analytics
    Views
    9,364
    Comparisons
    6,638
    Reviews
    9
    Average Words per Review
    1,217
    Rating
    7.7
    Comparisons
    Also Known As
    Amazon Managed Streaming for Apache Kafka
    Flink
    Learn More
    Overview

    Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

    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.

    Offer
    Learn more about Amazon MSK
    Learn more about Apache Flink
    Sample Customers
    Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
    LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company24%
    Comms Service Provider16%
    Media Company11%
    Financial Services Firm11%
    VISITORS READING REVIEWS
    Computer Software Company23%
    Comms Service Provider18%
    Financial Services Firm12%
    Media Company9%
    Company Size
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise72%
    REVIEWERS
    Small Business20%
    Midsize Enterprise20%
    Large Enterprise60%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise12%
    Large Enterprise71%
    Buyer's Guide
    Streaming Analytics
    July 2022
    Find out what your peers are saying about Databricks, Amazon, Microsoft and others in Streaming Analytics. Updated: July 2022.
    621,703 professionals have used our research since 2012.

    Amazon MSK is ranked 8th in Streaming Analytics with 1 review while Apache Flink is ranked 5th in Streaming Analytics with 9 reviews. Amazon MSK is rated 8.0, while Apache Flink is rated 7.6. The top reviewer of Amazon MSK writes "Allows you to build and run applications easily". On the other hand, the top reviewer of Apache Flink writes "Scalable framework for stateful streaming aggregations". Amazon MSK is most compared with Confluent, Amazon Kinesis, Azure Stream Analytics, Google Cloud Dataflow and Apache Spark Streaming, whereas Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Apache Spark Streaming and IBM Streams.

    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.