Amazon MSK vs Spring Cloud Data Flow comparison

Cancel
You must select at least 2 products to compare!
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Amazon MSK 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).
765,234 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
"Overall, it is very cost-effective based on the workflow.""MSK has a private network that's an out-of-box feature.""It is a stable product.""The most valuable feature of Amazon MSK is the integration.""Amazon MSK has significantly improved our organization by building seamless integration between systems.""It offers good stability."

More Amazon MSK Pros →

"The most valuable feature is real-time streaming.""The product is very user-friendly.""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."

More Spring Cloud Data Flow Pros →

Cons
"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers.""It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster.""Amazon MSK could improve on the features they offer. They are still lagging behind Confluence.""The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET.""The configuration seems a little complex and the documentation on the product is not available.""It should be more flexible, integration-wise."

More Amazon MSK Cons →

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required.""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.""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."

More Spring Cloud Data Flow Cons →

Pricing and Cost Advice
  • "The price of Amazon MSK is less than some competitor solutions, such as Confluence."
  • "The platform has better pricing than one of its competitors."
  • More Amazon MSK Pricing and Cost Advice →

  • "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."
  • More Spring Cloud Data Flow Pricing and Cost Advice →

    report
    Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
    765,234 professionals have used our research since 2012.
    Questions from the Community
    Top Answer:Amazon MSK has significantly improved our organization by building seamless integration between systems.
    Top Answer:The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET. They… more »
    Top Answer:We use the software to facilitate building integrations between systems.
    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
    6th
    out of 38 in Streaming Analytics
    Views
    8,163
    Comparisons
    6,453
    Reviews
    5
    Average Words per Review
    427
    Rating
    7.0
    10th
    out of 38 in Streaming Analytics
    Views
    3,998
    Comparisons
    3,018
    Reviews
    1
    Average Words per Review
    610
    Rating
    7.0
    Comparisons
    Also Known As
    Amazon Managed Streaming for Apache Kafka
    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.

    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.

    Sample Customers
    Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company18%
    Manufacturing Company8%
    Retailer5%
    VISITORS READING REVIEWS
    Financial Services Firm29%
    Computer Software Company16%
    Manufacturing Company7%
    Retailer7%
    Company Size
    VISITORS READING REVIEWS
    Small Business19%
    Midsize Enterprise12%
    Large Enterprise69%
    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.
    765,234 professionals have used our research since 2012.

    Amazon MSK is ranked 6th in Streaming Analytics with 6 reviews while Spring Cloud Data Flow is ranked 10th in Streaming Analytics with 5 reviews. Amazon MSK is rated 7.2, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Amazon MSK writes "Efficient real-time transaction tracking but time-consuming installation". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Amazon MSK is most compared with Confluent, Amazon Kinesis, Azure Stream Analytics and Google Cloud Dataflow, 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.