Apache Flink vs Spring Cloud Data Flow comparison

Apache and Broadcom are both solutions in the Streaming Analytics category. Apache is ranked #4 with an average rating of 8.0, while Broadcom is ranked #17 with an average rating of 7.5. Apache holds a 7.9% mindshare in SA, compared to Broadcom’s 2.6% 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

4,754 Views
4,754 Comparison Views

95% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

4,093 Views
1,392 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: June 2026).

Buyer's Guide

Apache Flink vs. Spring Cloud Data Flow

June 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

4th

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

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (31st)

Mindshare comparison

As of July 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 7.9%, down from 13.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 2.6%, down from 4.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	7.9%
Spring Cloud Data Flow	2.6%
Other	89.5%

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

"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."

"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."

"This is truly a real-time solution."

"We are very happy with the product, and we have been able to achieve all of the use cases that we are expected to deliver for our customers."

"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."

"Easy to deploy and manage."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

"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."

More Apache Flink pros

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

"The product is very user-friendly."

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

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

"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."

"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 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."

"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

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

"Apache Flink's documentation should be available in more languages."

"Failure is another area where it is a bit rigid or not that flexible."

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

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"Apache Flink should improve its data capability and data migration."

"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."

"Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing."

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."

"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."

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

"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 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."

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

"Some of the features, like the monitoring tools, are not very mature and are still evolving."

More Spring Cloud Data Flow cons

Pricing and Cost Advice

"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."

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

"It's an open source."

"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."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

13%

Computer Software Company

Manufacturing Company

Financial Services Firm

17%

Computer Software Company

11%

Retailer

Outsourcing 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 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 advice do you have for others considering Apache Flink?

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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...

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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Apache Flink

July 2026

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Buyer's Guide

Spring Cloud Data Flow

June 2026

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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

June 2026

Free Report: Apache Flink vs. Spring Cloud Data Flow

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