Apache Pulsar vs Spring Cloud Data Flow comparison

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Executive Summary

We performed a comparison between Apache Pulsar and Spring Cloud Data Flow based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon, Confluent and others in Streaming Analytics.
To learn more, read our detailed Streaming Analytics Report (Updated: March 2024).
765,386 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The solution operates as a classic message broker but also as a streaming platform."

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"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like.""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.""The product is very user-friendly."

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Cons
"Documentation is poor because much of it is in Chinese with no English translation."

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"Some of the features, like the monitoring tools, are not very mature and are still evolving.""On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required.""Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications.""The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

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Pricing and Cost Advice
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  • "This is an open-source product that can be used free of charge."
  • "If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
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    Questions from the Community
    Top Answer:The solution operates as a classic message broker but also as a streaming platform.
    Top Answer:The solution is open-source freeware.
    Top Answer:Documentation is poor because much of it is in Chinese with no English translation.
    Top Answer:Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD… more »
    Top Answer:Spring Cloud Data Flow is used for asynchronous workloads. We are working on streams. For example, a workload is generated at a particular point, and at the source, it gets passed down through a… more »
    Top Answer:The solution requires little maintenance. My advice to others is for them to follow the documentation. The solution is very well-designed and they deliver on their promises. I rate Spring Cloud Data… more »
    Ranking
    12th
    out of 38 in Streaming Analytics
    Views
    1,579
    Comparisons
    1,077
    Reviews
    1
    Average Words per Review
    470
    Rating
    8.0
    10th
    out of 38 in Streaming Analytics
    Views
    3,998
    Comparisons
    3,018
    Reviews
    1
    Average Words per Review
    610
    Rating
    7.0
    Comparisons
    Learn More
    Pulsar
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    Overview

    Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project

    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.

    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company24%
    Financial Services Firm11%
    Manufacturing Company8%
    Government7%
    VISITORS READING REVIEWS
    Financial Services Firm29%
    Computer Software Company16%
    Manufacturing Company7%
    Retailer7%
    Company Size
    VISITORS READING REVIEWS
    Small Business22%
    Midsize Enterprise17%
    Large Enterprise61%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise9%
    Large Enterprise77%
    Buyer's Guide
    Streaming Analytics
    March 2024
    Find out what your peers are saying about Databricks, Amazon, Confluent and others in Streaming Analytics. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Apache Pulsar is ranked 12th in Streaming Analytics with 1 review while Spring Cloud Data Flow is ranked 10th in Streaming Analytics with 5 reviews. Apache Pulsar is rated 8.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Apache Pulsar writes "The solution can mimic other APIs without changing a line of code". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Apache Pulsar is most compared with Apache Flink, Apache Spark Streaming, Amazon Kinesis, Amazon MSK and Azure Stream Analytics, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Mule Anypoint Platform.

    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.