Confluent vs Spring Cloud Data Flow comparison

Confluent and VMware are both solutions in the Streaming Analytics category. Confluent is ranked #3 with an average rating of 8.3, while VMware is ranked #9 with an average rating of 7.7. Confluent holds a 8.5% mindshare in SA, compared to VMware’s 4.9% mindshare. Additionally, 100% of Confluent users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Confluent

Read 22 Confluent reviews

6,102 Views
4,639 Comparison Views

100% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,498 Views
1,859 Comparison Views

88% willing to recommend

Confluent

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 17, 2024

Confluent and Spring Cloud Data Flow compete in data streaming and processing. Confluent has the edge with robust support and pricing options, yet Spring Cloud Data Flow is seen as superior due to its rich feature set, making it a compelling choice.

Features: Confluent offers Apache Kafka integration, multi-cloud capabilities, and a comprehensive library for stream processing, ideal for scalability and high throughput. Spring Cloud Data Flow focuses on data pipeline orchestration with support for a wide range of data services, enabling effective modeling, deployment, and monitoring of data processes. Key differences lie in Spring Cloud Data Flow's orchestration capabilities compared to Confluent's emphasis on smooth Kafka-based streaming.

Room for Improvement: Confluent could enhance ease of navigating its comprehensive feature set, streamline infrastructure management, and improve integration with competitor solutions. Spring Cloud Data Flow may benefit from reduced complexity in deployment, expanded support options, and scalability enhancements in diverse environments.

Ease of Deployment and Customer Service: Confluent offers easy deployment with a cloud-first approach, backed by extensive documentation and dedicated support, easing Kafka operations. Spring Cloud Data Flow adopts a microservices deployment model across environments, though technical expertise is required initially. Confluent's customer service is often more responsive and accessible compared to Spring Cloud Data Flow's community-driven support.

Pricing and ROI: Confluent features a competitive pricing structure with managed service options, leading to potentially lower total ownership costs and faster ROI. Spring Cloud Data Flow is open-source, minimizing setup expenses but might incur longer-term integration and support costs. Confluent enables quicker ROI through managed solutions, although Spring Cloud Data Flow can be cost-efficient for organizations with pre-existing internal capabilities.

To learn more, read our detailed Confluent vs. Spring Cloud Data Flow Report (Updated: January 2025).

Buyer's Guide

Confluent vs. Spring Cloud Data Flow

January 2025

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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

Confluent

Ranking in Streaming Analytics

3rd

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

9th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (23rd)

Mindshare comparison

As of February 2025, in the Streaming Analytics category, the mindshare of Confluent is 8.5%, down from 11.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.9%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Yantao Zhao

Software Integration Engineer at Thales

Great tool for sharing knowledge, internal communication and allows for real-time collaboration on pages

Confluence is easy to use and modify. However, sometimes there are too many pages. We have to reorganize the folder or parent account. Since everyone can create a page, the same knowledge might be created in multiple places by different people. This leads to redundancy and makes it difficult to find information. It's not centralized. So it could be more user-friendly and centralized. A way to reduce redundancy would be helpful. It's very easy to use, so everyone can create knowledge. But it would be good to synchronize and organize that information a bit better. Another improvement would be in Confluence search. You can search for keywords, but it's not like AI, not even ChatGPT or OpenAI. It would be nice to get more relevant or organized answers. If you're outside the company, you just get some titles containing the keyword you input. But if Confluence were like a database, you could input something and get a well-organized search offering from multiple pages.

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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

"I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and tools."

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

"The monitoring module is impressive."

"We mostly use the solution's message queues and event-driven architecture."

"The solution can handle a high volume of data because it works and scales well."

"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."

"Their tech support is amazing; they are very good, both on and off-site."

More Confluent 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 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 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 product is very user-friendly."

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

"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."

Cons

"Confluent's price needs improvement."

"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."

"They should remove Zookeeper because of security issues."

"there is room for improvement in the visualization."

"It could have more themes. They should also have more reporting-oriented plugins as well. It would be great to have free custom reports that can be dispatched directly from Jira."

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."

"The pricing model should include the ability to pick features and be charged for them only."

"It would help if the knowledge based documents in the support portal could be available for public use as well."

More Confluent 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."

"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."

"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."

"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."

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

Pricing and Cost Advice

"You have to pay additional for one or two features."

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."

"Confluent is highly priced."

"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"It comes with a high cost."

More Confluent pricing and cost advice

"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."

"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

19%

Computer Software Company

17%

Manufacturing Company

Insurance Company

Financial Services Firm

27%

Computer Software Company

18%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Confluent?

I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...

See all answers

What is your experience regarding pricing and costs for Confluent?

Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenanc...

See all answers

What needs improvement with Confluent?

One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it c...

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

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Compared 39% of the time

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Compared 11% of the time

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Compared 9% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 5% of the time

Azure Data Factory vs Spring Cloud Data Flow

Compared 4% of the time

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Product Reports

Buyer's Guide

Confluent

February 2025

Download Confluent product report

Buyer's Guide

Spring Cloud Data Flow

January 2025

Download Spring Cloud Data Flow product report

Overview

Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.

Confluent has integrated cutting-edge features that are designed to enhance these tasks:

Speed up application development and connectivity
Enable transformations through stream processing
Streamline business operations at scale
Adhere to strict architectural standards

Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.

Confluent's software is available in three different varieties:

A free, open-source streaming platform that makes it simple to start using real-time data streams
An enterprise-grade version of the product with more administrative, ops, and monitoring tools
A premium cloud-based version.

Confluent Advantage Features

Confluent has many valuable key features. Some of the most useful ones include:

Multi-language
- Clients: C++, Python, Go, and .NET
- REST proxy: Can connect to Kafka from any connected network device
- Admin REST APIs: RESTful interface for performing administrator operations

Pre-built ecosystem
- Connectors: More than 100 supported connectors, including S3, Elastic, HDFS, JDBC
- MQTT proxy: Gain access to Kafka from MQTT gateways and devices
- Schema registry: Centralized database to guarantee data compatibility

Streaming database
- ksqlDB: Materialized views and real-time stream processing

GUI management
- Control panel: GUI for scalable Kafka management and monitoring
- Health+: Smart alerts and cloud-based control centers

DevOps automation that is flexible
- Confluent for Kubernetes: Complete API to deploy on Kubernetes
- Automated Ansible deployment on non-containerized environments

Dynamic performance
- Self-balancing clusters: Automated partition re-balancing across brokers in the cluster
- Tiered storage: Older Kafka data offloading to object storage with transparent access

Security that is enterprise-grade
- Role-based access control: Granular user/group access authorization
- Audit logs that are structured: Logs of user actions kept in dedicated Kafka topics
- Secret protection: Sensitive information is encrypted

Global resilience
- Linking clusters: A real-time, highly reliable, and consistent bridge across on-premises and cloud environments
- Multiple-region clusters: Single Kafka cluster with automated client failover distributed across multiple data centers
- Replicator: Asynchronous replication that is based on the Kafka Connect framework

Support
- Round the clock enterprise support from Kafka experts

Reviews from Real Users

Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:

Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”

Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”

Confluent

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.

VMware

Sample Customers

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost

Information Not Available

Buyer's Guide

Confluent vs. Spring Cloud Data Flow

January 2025

Free Report: Confluent vs. Spring Cloud Data Flow

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