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 #11 with an average rating of 7.8. Apache holds a 11.3% mindshare in SA, compared to Broadcom’s 3.8% 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

3,681 Views
3,681 Comparison Views

95% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,126 Views
1,250 Comparison Views

88% willing to recommend

Apache Flink

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 21, 2025

Spring Cloud Data Flow and Apache Flink are competitors in the data processing category. Spring Cloud Data Flow seems to have the upper hand in terms of pricing and support, while Apache Flink is more appealing for its comprehensive feature set aimed at organizations seeking advanced solutions.

Features: Spring Cloud Data Flow integrates seamlessly with the Spring ecosystem, streamlining data pipeline development and monitoring. It supports microservice-based architecture well. Apache Flink is highly regarded for its stream processing capabilities, which include both real-time and batch processing, offering high performance and scalability in distributed data execution.

Room for Improvement: Spring Cloud Data Flow could enhance its real-time processing features and expand its integrations beyond the Spring ecosystem. Improving its handling of complex data workflows would add value. Apache Flink’s learning curve could be reduced, and enhanced beginner-friendly documentation would be beneficial. It would gain from better default configurations to streamline initial deployments.

Ease of Deployment and Customer Service: Spring Cloud Data Flow offers a user-friendly deployment process, particularly for Spring users, backed by extensive documentation and a supportive community. Apache Flink, while powerful, introduces complexity in deployment, needing skilled users for optimal results. It relies heavily on community support, which emphasizes interactions among experienced users.

Pricing and ROI: Spring Cloud Data Flow is preferred in cost-sensitive scenarios due to its lower initial expenditure, especially within Spring-adopted environments. Apache Flink, though requiring a larger initial outlay, delivers long-term value by improving insights and processing complex data efficiently, translating to advantageous ROI for businesses prioritizing sophisticated data handling.

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

Buyer's Guide

Apache Flink vs. Spring Cloud Data Flow

February 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

11th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (20th)

Mindshare comparison

As of February 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 11.3%, down from 12.1% compared to the previous year. The mindshare of Spring Cloud Data Flow is 3.8%, down from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Flink	11.3%
Spring Cloud Data Flow	3.8%
Other	84.9%

Streaming Analytics

Featured Reviews

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

Enables robust real-time data processing but documentation needs refinement

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. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

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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

"Apache Flink provides faster and low-cost investment for me; I find it to have low hardware requirements, and it's faster with low code, meaning it's easy to understand for moving the streaming data."

"Easy to deploy and manage."

"What I appreciate best about Apache Flink is that it's open source and geared towards a distributed stream processing framework."

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

"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."

"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."

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

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

More Apache Flink pros

"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."

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

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

"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 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 solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."

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

Cons

"There are more libraries that are missing and also maybe more capabilities for machine learning."

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

"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

"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."

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

"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."

"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."

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

More Apache Flink cons

"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."

"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."

"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 solution's community support could be improved."

"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."

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."

"It's an open-source solution."

"This is an open-source platform 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."

"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

20%

Retailer

12%

Computer Software Company

10%

Manufacturing Company

Financial Services Firm

20%

Computer Software Company

12%

Retailer

Healthcare 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

February 2026

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Buyer's Guide

Spring Cloud Data Flow

January 2026

Download Spring Cloud Data Flow product report

Also Known As

Flink

No data available

Overview

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

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

February 2026

Free Report: Apache Flink vs. Spring Cloud Data Flow

Find out what your peers are saying about Apache Flink vs. Spring Cloud Data Flow and other solutions. Updated: February 2026.

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