Apache Flink vs Spring Cloud Data Flow comparison

Apache and Broadcom are both solutions in the Streaming Analytics category. Apache is ranked #5 with an average rating of 7.8, while Broadcom is ranked #10 with an average rating of 7.8. Apache holds a 13.4% mindshare in SA, compared to Broadcom’s 4.5% 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

3,674 Views
3,674 Comparison Views

95% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,005 Views
1,322 Comparison Views

88% willing to recommend

Apache Flink

Spring Cloud Data Flow

Comparison Buyer's Guide

Download the report

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: December 2025).

Buyer's Guide

Apache Flink vs. Spring Cloud Data Flow

December 2025

Download the complete report

Helped 879,371 peers since 2012

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

5th

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

10th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (21st)

Mindshare comparison

As of December 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 13.4%, up from 11.4% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.5%, 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	13.4%
Spring Cloud Data Flow	4.5%
Other	82.1%

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.

Read full review

Lipika Naik

Senior Software Engineer at QBE Regional Insurance

Provides ease of integration with other cloud platforms

Spring Cloud Data Flow is a useful product if I consider how there are different providers with whom my company had to deal, and most of them offer cloud-based products. I can't explain any crucial circumstances where the product's integration capabilities were helpful, but the aforementioned details explain the scenario for which I used the solution. I was only involved with the development of the product and not with the data pipeline configuration phase. The use of Spring Cloud Data Flow greatly impacted projects' time to market since our company's intention was to actually deploy and ensure that the payment platform integrated with it, which was an easy process. The product's user interface was very intuitive. The tool was deployed in multiple environments, but I am not sure about the production. From the time I started taking up the job in my current organization, I saw that we have deployed the tool in multiple environments wherein the number of users extensively used the product in the UAT environment, which is one of the most stable environments. There were 20 different methods to test the tool. I wouldn't be able to tell you the production details of the tool as I was more part of the production deployment, but I can say that it was deployed with the intent of making it available for 10,000 users. Those who plan to use the product should enjoy the flexibility of the solution. I rate the tool a nine out of ten.

Read full review

Quotes from Members

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

Pros

"This is truly a real-time solution."

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

"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."

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

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

"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."

"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."

"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."

More Apache Flink pros

"The product is very user-friendly."

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

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

"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 most valuable feature is real-time streaming."

"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 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."

Cons

"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."

"The technical support from Apache is not good; support needs to be improved. I would rate them from one to ten as not good."

"The machine learning library is not very flexible."

"The solution could be more user-friendly."

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

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

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

"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

"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."

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

"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."

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

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

"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."

Pricing and Cost Advice

"It's an open-source solution."

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

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

"It's an open source."

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

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

"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."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

879,371 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

20%

Retailer

12%

Computer Software Company

10%

Manufacturing Company

Financial Services Firm

22%

Computer Software Company

13%

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 do you like most about Apache Flink?

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. ...

See all answers

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...

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

Comparisons

Databricks vs Apache Flink

Compared 14% of the time

Amazon Kinesis vs Apache Flink

Compared 10% of the time

Confluent vs Apache Flink

Compared 10% of the time

Azure Stream Analytics vs Apache Flink

Compared 8% of the time

Google Cloud Dataflow vs Apache Flink

Compared 7% of the time

More Apache Flink Competitors

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 7% of the time

Apache Kafka vs Spring Cloud Data Flow

Compared 5% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 5% of the time

WSO2 Enterprise Integrator vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Apache Flink

December 2025

Download Apache Flink product report

Buyer's Guide

Spring Cloud Data Flow

December 2025

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

December 2025

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: December 2025.

DOWNLOAD NOW

879,371 professionals have used our research since 2012.

See our Apache Flink vs. Spring Cloud Data Flow report.

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.