Apache Kafka vs Spring Cloud Data Flow comparison

Why PeerSpot?

Apache and Broadcom are both solutions in the Streaming Analytics category. Apache is ranked #6 with an average rating of 8.6, while Broadcom is ranked #17 with an average rating of 8.0. Apache holds a 3.5% mindshare in SA, compared to Broadcom’s 2.6% mindshare. Additionally, 96% of Apache users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Apache Kafka

Read 92 Apache Kafka reviews

7,712 Views
4,010 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

5,525 Views
1,658 Comparison Views

88% willing to recommend

Apache Kafka

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Apache Kafka and Spring Cloud Data Flow are contenders in the streaming and processing landscape. Apache Kafka often takes the lead due to its robust messaging capabilities and scalability, essential for handling high-throughput messaging applications.

Features: Apache Kafka is celebrated for its replication, enabling high availability and data safety during failures. It also excels in partitioning, allowing for parallel processing using tools like Apache Spark, boosting throughput. Kafka's integration capabilities with various platforms make it versatile. Spring Cloud Data Flow is lauded for its simple programming model and integration features, making application orchestration straightforward. Its auto-configuration ensures quick setup and flexibility.

Room for Improvement: Apache Kafka can improve by simplifying its complex setup, enhancing its GUI, and better integrating monitoring tools to ease management. Spring Cloud Data Flow could benefit from a more interactive user interface and better documentation. Improving deployment pipelines and expanding technical support would also enhance the user experience.

Ease of Deployment and Customer Service: Apache Kafka offers on-premises and cloud deployment options but relies on community support unless enterprise packages are used. Handling deployments often requires expert management. Spring Cloud Data Flow also provides flexible deployment but struggles with community and technical support, largely depending on user contributions.

Pricing and ROI: Apache Kafka’s open-source nature offers cost savings by eliminating licensing fees, although managing deployments might incur additional costs. Using platforms like Confluent can increase expenses but often justifies high ROI through performance. Spring Cloud Data Flow, being open-source, minimizes initial costs, but any premium support options add expenses. Users report good ROI due to operational efficiency and minimized licensing needs.

To learn more, read our detailed Apache Kafka vs. Spring Cloud Data Flow Report (Updated: September 2026).

Buyer's Guide

Apache Kafka vs. Spring Cloud Data Flow

September 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Kafka

Ranking in Streaming Analytics

6th

Average Rating

8.2

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (33rd)

Mindshare comparison

As of November 2026, in the Streaming Analytics category, the mindshare of Apache Kafka is 3.5%, down from 3.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 2.6%, down from 4.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Kafka	3.5%
Spring Cloud Data Flow	2.6%
Other	93.9%

Streaming Analytics

Featured Reviews

Amandeep Pawar

Engineer Technology at Iris Software Technologies Private Limited

Event-driven architecture has improved asynchronous communication and supports high throughput

We can improve the high throughput because there are some limitations. We could add something to improve Apache Kafka. Based on my daily usage and analysis, there is a complex setup and management, which is one area requiring improvement. Apache Kafka does not have built-in message delay or scheduling capabilities. Apache Kafka cannot natively schedule messages. Limited message prioritization is also an area requiring improvement. Ordering is limited to a single partition. Large messages affect performance. Apache Kafka is optimized for many small to medium-sized messages, but large payloads increase network usage, increase disk usage, and slow down producers and consumers. I rate Apache Kafka eight out of ten instead of ten out of ten because operating an Apache Kafka cluster requires expertise in partitioning, application monitoring, and capacity planning. Self-managed deployment can become complex as the cluster grows. Pro-managed Apache Kafka services significantly reduce the operational overhead.

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NitinGoyal

Engineering Lead at Naukri.com

Has a plug-and-play model and provides good robustness and scalability

The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

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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

"The most valuable features of the solution revolve around areas like the latency part, where the tool offers very little latency and the sequencing part."

"I like the performance and reliability of Kafka, as I needed a data streaming buffer that could handle thousands of messages per second with at least one processing point for an analytics pipeline, and Kafka fits this requirement very well, as it is a fast, distributed message broker that does exactly what it is designed to do."

"The most valuable feature is the performance."

"It is easy to configure."

"Performance-wise, Kafka is better than any of the other products."

"The most valuable features are the stream API, consumer groups, and the way that the scaling takes place."

"Kafka provides us with a way to store the data used for analytics. That's the big selling point. There's very good log management."

"Resiliency is great and also the fact that it handles different data formats."

More Apache Kafka pros

"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."

"The dashboards in Spring Cloud Dataflow are quite valuable."

"The most valuable feature is real-time streaming."

"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."

"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 product is very user-friendly."

"Overall, Spring Cloud Data Flow is a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon."

"This product will assist us in saving costs in many ways: No longer need to continue paying high fees for proprietary software, reduce the number of software engineers needed to support the product, and achieve faster time to market by using this product for our middleware."

More Spring Cloud Data Flow pros

Cons

"The standard Kafka Java library, which is shipped with the product, is too complex for inexperienced users."

"The solution can improve by having automation for developers. We have done many manual calculations and it has been difficult but if it was automated it would be much better."

"Data pulling and restart ability need improving."

"The graphical user environment is currently lacking in Apache."

"If the graphical user interface was easier for the Kafka administration it would be much better. Right now, you need to use the program with the command-line interface. If the graphical user interface was easier, it could be a better product."

"I would like to see real-time event-based consumption of messages rather than the traditional way through a loop."

"It's not possible to substitute IBM MQ with Apache Kafka because the JMS part is not very stable."

"Lacks elasticity and the ability to scale down."

More Apache Kafka cons

"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 visual user interface could use some help; it needs improvement."

"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."

"The solution's community support could be improved."

"The documentation on offer is not that good."

"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

"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 is not an easy-to-use tool, so improvements are required."

More Spring Cloud Data Flow cons

Pricing and Cost Advice

"I rate Apache Kafka's pricing a five on a scale of one to ten, where one is cheap and ten is expensive. There are no additional costs apart from the licensing fees for Apache Kafka."

"The solution is open source."

"The solution is open source; it's free to use."

"Apache Kafka is free."

"It's a bit cheaper compared to other Q applications."

"The solution is free, it is open-source."

"The price for the enterprise version is quite high. For on-premise, there is an annual fee, which starts at 60,000 euros, but it is usually higher than 100,000 euros. The cost for a project including the subscription is usually between 100,000 to 200,000 euros. The cost also depends on the level of support. There are two different levels of support."

"I would not subscribe to the Confluent platform, but rather stay on the free open source version. The extra cost wasn't justified."

More Apache Kafka pricing and cost advice

"The solution provides value for money, and we are currently using its community edition."

"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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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

13%

Manufacturing Company

10%

Construction Company

Financial Services Firm

15%

Outsourcing Company

Computer Software Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	33
Midsize Enterprise	20
Large Enterprise	51

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	1
Large Enterprise	5

Questions from the Community

What are the differences between Apache Kafka and IBM MQ?

Apache Kafka is open source and can be used for free. It has very good log management and has a way to store the data used for analytics. Apache Kafka is very good if you have a high number of user...

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What is your experience regarding pricing and costs for Apache Kafka?

Based on my understanding for the setup and cost to set up Apache Kafka, I was not responsible for the pricing or licensing decisions. Apache Kafka itself is open source and free to use. Infrastruc...

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What needs improvement with Apache Kafka?

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What needs improvement with Spring Cloud Data Flow?

There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or r...

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What is your primary use case for Spring Cloud Data Flow?

We had a project for content management, which involved multiple applications each handling content ingestion, transformation, enrichment, and storage for different customers independently. We want...

See all answers

What advice do you have for others considering Spring Cloud Data Flow?

I would definitely recommend Spring Cloud Data Flow. It requires minimal additional effort or time to understand how it works, and even non-specialists can use it effectively with its friendly docu...

See all answers

Comparisons

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Compared 11% of the time

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Compared 9% of the time

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Compared 8% of the time

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Compared 7% of the time

IBM MQ vs Apache Kafka

Compared 7% of the time

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Compared 6% of the time

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Compared 6% of the time

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Compared 5% of the time

StreamSets vs Spring Cloud Data Flow

Compared 4% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 4% of the time

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Product Reports

Buyer's Guide

Apache Kafka

October 2026

Download Apache Kafka product report

Buyer's Guide

Spring Cloud Data Flow

October 2026

Download Spring Cloud Data Flow product report

Overview

Apache Kafka provides scalable, high-throughput, real-time data processing. Appreciated for its open-source nature and integration capabilities, Kafka supports distributed messaging and high-volume handling with essential features like message retention, replication, and partitioning.

Apache Kafka is a powerful tool for managing efficient data streams and high volumes of asynchronous messages. Its ease of setup and robust integration options make it popular among industries requiring real-time data streaming and processing. Key features such as message retention and consumer groups cater to demanding applications, while fault-tolerant design ensures reliability. Despite its advantages, Kafka can improve in areas like duplicate management, documentation, and intuitive interfaces. Challenges in configuration and monitoring tools suggest areas for enhancement, alongside reducing complexity and resource dependency.

What are the key features of Apache Kafka?

Scalability: Efficiently handles increasing data volumes without performance loss.
High Throughput: Processes large amounts of data quickly and efficiently.
Real-time Processing: Facilitates immediate data streaming and analytics.
Fault Tolerance: Maintains operations despite failures, ensuring continuous data flow.
Open-source Nature: Offers community-driven enhancements and reduced costs.

What benefits should users look for in Apache Kafka reviews?

Robust Integration: Easily connects with various applications and systems.
Cost-effectiveness: Leverages open-source advantages for financial savings.
Reliability: Provides consistent performance with fault-tolerant mechanisms.
Scalable Infrastructure: Supports growing business needs without compromising efficiency.

Industry applications for Apache Kafka include real-time data streaming for IoT, big data management, and analytics. In finance, it supports fraud detection and transaction monitoring. Healthcare uses Kafka for patient data handling and logistics leverage its data distribution capabilities to optimize operations. Its ability to manage large-scale asynchronous communication makes it vital across sectors demanding high data throughput and reliability.

Apache

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.

Broadcom

Sample Customers

Uber, Netflix, Activision, Spotify, Slack, Pinterest

Information Not Available

Buyer's Guide

Apache Kafka vs. Spring Cloud Data Flow

September 2026

Free Report: Apache Kafka vs. Spring Cloud Data Flow

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