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Apache Flink vs Spring Cloud Data Flow comparison

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Featured Review
Find out what your peers are saying about Apache Flink vs. Spring Cloud Data Flow and other solutions. Updated: January 2022.
564,643 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:
Pros
"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 is user-friendly and the reporting is good.""The documentation is very good.""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.""This is truly a real-time solution.""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.""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 setup was not too difficult."

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"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure.""The most valuable feature is real-time streaming."

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Cons
"There is a learning curve. It takes time to learn.""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.""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 machine learning library is not very flexible.""We have a machine learning team that works with Python, but Apache Flink does not have full support for the language.""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 a future release, they could improve on making the error descriptions more clear."

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"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation.""Some of the features, like the monitoring tools, are not very mature and are still evolving."

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Pricing and Cost Advice
  • "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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  • "This is an open-source product that can be used free of charge."
  • More Spring Cloud Data Flow Pricing and Cost Advice →

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    Questions from the Community
    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 »
    Top Answer: 
    Hi @Mohammad Masudu Rahaman, @Saket Puranik, @MahmoudAbu-Ghali and @Fabio Ferri, Can you chime in here to share your experience and expertise?​
    Ranking
    4th
    out of 38 in Streaming Analytics
    Views
    7,130
    Comparisons
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    Reviews
    9
    Average Words per Review
    1,217
    Rating
    7.7
    7th
    out of 38 in Streaming Analytics
    Views
    7,279
    Comparisons
    5,737
    Reviews
    2
    Average Words per Review
    1,101
    Rating
    8.0
    Comparisons
    Also Known As
    Flink
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    Overview

    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.

    Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
    Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

    Offer
    Learn more about Apache Flink
    Learn more about Spring Cloud Data Flow
    Sample Customers
    LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company26%
    Comms Service Provider20%
    Media Company11%
    Financial Services Firm10%
    VISITORS READING REVIEWS
    Computer Software Company28%
    Comms Service Provider15%
    Financial Services Firm14%
    Retailer6%
    Company Size
    REVIEWERS
    Small Business22%
    Midsize Enterprise11%
    Large Enterprise67%
    No Data Available
    Find out what your peers are saying about Apache Flink vs. Spring Cloud Data Flow and other solutions. Updated: January 2022.
    564,643 professionals have used our research since 2012.

    Apache Flink is ranked 4th in Streaming Analytics with 9 reviews while Spring Cloud Data Flow is ranked 7th in Streaming Analytics with 2 reviews. Apache Flink is rated 7.6, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Apache Flink writes "Scalable framework for stateful streaming aggregations". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Apache Flink is most compared with Amazon Kinesis, Azure Stream Analytics, Databricks, Google Cloud Dataflow and Apache Pulsar, whereas Spring Cloud Data Flow is most compared with TIBCO BusinessWorks, Mule Anypoint Platform, Cloudera DataFlow, Apache Spark Streaming and StreamSets. See our Apache Flink vs. Spring Cloud Data Flow report.

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