Apache Flink vs Spring Cloud Data Flow comparison

Apache and Broadcom are both solutions in the Streaming Analytics category. Apache is ranked #3 with an average rating of 8.0, while Broadcom is ranked #16 with an average rating of 7.5. Apache holds a 8.9% mindshare in SA, compared to Broadcom’s 2.9% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

4,343 Views
4,343 Comparison Views

95% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,699 Views
1,295 Comparison Views

88% willing to recommend

Apache Flink

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive SummaryUpdated on Mar 22, 2026

Spring Cloud Data Flow and Apache Flink are data processing platforms with Spring Cloud Data Flow excelling in microservices orchestration while Apache Flink is superior for real-time batch and stream processing due to its robust features and scalability, making it the preferable choice for complex real-time processing.

Features: Spring Cloud Data Flow offers ease of orchestration for stream and task processing, strong integration with Spring ecosystem tools, and fine-grained pipeline control. Apache Flink provides advanced stream processing, state management, and high throughput for real-time applications.

Room for Improvement: Spring Cloud Data Flow could improve by enhancing real-time processing capabilities and offering greater scalability and flexibility. Apache Flink may benefit from simplifying its complex configuration process, providing more robust deployment documentation, and refining ease of use for new users.

Ease of Deployment and Customer Service: Spring Cloud Data Flow supports rapid deployment within the Spring ecosystem, benefiting from substantial support and seamless integration. Apache Flink has a more complex setup with solid community support and extensive documentation, providing powerful processing benefits once deployed.

Pricing and ROI: Spring Cloud Data Flow offers lower initial setup costs with quick ROI using existing infrastructure. Apache Flink involves higher initial investment due to complexity but provides strong returns for advanced streaming analytics, making it cost-effective for intensive applications.

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

Buyer's Guide

Apache Flink vs. Spring Cloud Data Flow

April 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 Flink

Ranking in Streaming Analytics

3rd

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

16th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (30th)

Mindshare comparison

As of May 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 8.9%, down from 13.7% compared to the previous year. The mindshare of Spring Cloud Data Flow is 2.9%, down from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	8.9%
Spring Cloud Data Flow	2.9%
Other	88.2%

Streaming Analytics

Featured Reviews

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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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 documentation is very good."

"Among all of this, if I would talk about streaming, Apache Flink wins hands down, but there are other products like Apache Pulsar which I have no idea."

"It is user-friendly and the reporting is good."

"The end-to-end latency was drastically reduced, and our capability of handling high throughput has increased by using Flink."

"Allows us to process batch data, stream to real-time and build pipelines."

"The ease of usage, even for complex tasks, stands out."

"Flink moved on to becoming a standard technology for location platform."

"Apache Flink's best feature is its data streaming tool."

More Apache Flink pros

"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."

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

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

"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."

"The product is very user-friendly."

"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 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."

More Spring Cloud Data Flow pros

Cons

"One way to improve Flink would be to enhance integration between different ecosystems."

"There is room for improvement in the initial setup process."

"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."

"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."

"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."

"There is a learning curve. It takes time to learn."

"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."

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

More Apache Flink cons

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

"I would improve the dashboard features as they are not very user-friendly."

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

"Spring Cloud Data Flow is not an easy-to-use tool, so 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 documentation on offer is not that good."

"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."

More Spring Cloud Data Flow cons

Pricing and Cost Advice

"It's an open-source solution."

"Apache Flink is open source so we pay no licensing for the use of the software."

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

"It's an open source."

"This is an open-source platform that can be used free of charge."

"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

18%

Retailer

13%

Computer Software Company

Manufacturing Company

Financial Services Firm

18%

Computer Software Company

10%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	12

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

Questions from the Community

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

The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with Apache Flink?

Apache could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

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What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

See all answers

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...

See all answers

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

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Product Reports

Buyer's Guide

Apache Flink

May 2026

Download Apache Flink product report

Buyer's Guide

Spring Cloud Data Flow

May 2026

Download Spring Cloud Data Flow product report

Also Known As

Flink

No data available

Overview

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

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

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

Information Not Available

Buyer's Guide

Apache Flink vs. Spring Cloud Data Flow

April 2026

Free Report: Apache Flink vs. Spring Cloud Data Flow

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