Cloudera DataFlow vs Spring Cloud Data Flow comparison

Cloudera and Broadcom are both solutions in the Streaming Analytics category. Cloudera is ranked #19 with an average rating of 8.5, while Broadcom is ranked #11 with an average rating of 7.8. Cloudera holds a 1.6% mindshare in SA, compared to Broadcom’s 4.1% mindshare. Additionally, 80% of Cloudera users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Cloudera DataFlow

Read 5 Cloudera DataFlow reviews

804 Views
724 Comparison Views

80% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,055 Views
1,283 Comparison Views

88% willing to recommend

Cloudera DataFlow

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 17, 2024

Spring Cloud Data Flow and Cloudera DataFlow are competing products in the data streaming and processing category. Spring Cloud Data Flow has an edge in integration and flexibility, while Cloudera DataFlow offers a comprehensive feature set for advanced analytics.

Features: Spring Cloud Data Flow features include a flexible deployment model, robust integration capabilities, and support for various message brokers. Cloudera DataFlow is noted for its exceptional scalability, rich data analytics capabilities, and extensive automation support systems.

Room for Improvement: Spring Cloud Data Flow could benefit from enhanced scalability for larger implementations, improved data analytics capabilities, and better out-of-the-box competitive features. Cloudera DataFlow can improve by simplifying its deployment model, enhancing ease of use, and broadening integration with more diverse third-party platforms.

Ease of Deployment and Customer Service: Spring Cloud Data Flow is renowned for seamless deployment and strong community support, making it user-friendly for developers. Cloudera DataFlow offers robust enterprise-level deployment solutions coupled with dedicated customer service, suitable for expansive enterprise needs.

Pricing and ROI: Spring Cloud Data Flow offers cost-effective solutions with lower initial setup costs, attracting businesses with flexible financial strategies. Cloudera DataFlow justifies its higher pricing with enhanced capabilities, promising better ROI for enterprises demanding a comprehensive and scalable analytics framework.

To learn more, read our detailed Cloudera DataFlow vs. Spring Cloud Data Flow Report (Updated: December 2025).

Buyer's Guide

Cloudera DataFlow vs. Spring Cloud Data Flow

December 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

Cloudera DataFlow

Ranking in Streaming Analytics

19th

Average Rating

7.4

Reviews Sentiment

6.5

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 January 2026, in the Streaming Analytics category, the mindshare of Cloudera DataFlow is 1.6%, up from 1.2% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.1%, 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 (%)
Spring Cloud Data Flow	4.1%
Cloudera DataFlow	1.6%
Other	94.3%

Streaming Analytics

Featured Reviews

Mohamed-Saied

Senior Data Architect at Teradata Corporation

Efficient data integration and workflow scheduling elevate project performance

Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…

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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.

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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

"DataFlow's performance is okay."

"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."

"The most effective features are data management and analytics."

"The initial setup was not so difficult"

"This solution is very scalable and robust."

"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 most valuable feature is real-time streaming."

"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 dashboards in Spring Cloud Dataflow are quite valuable."

"The product is very user-friendly."

"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 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."

Cons

"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."

"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."

"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."

"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."

"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."

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

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

"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 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."

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."

"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

No data available

Financial Services Firm

20%

Computer Software Company

13%

Retailer

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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

Questions from the Community

What do you like most about Cloudera DataFlow?

The most effective features are data management and analytics.

See all answers

What needs improvement with Cloudera DataFlow?

Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.

See all answers

What is your primary use case for Cloudera DataFlow?

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

Streaming Analytics

January 2026

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Buyer's Guide

Spring Cloud Data Flow

January 2026

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Also Known As

CDF, Hortonworks DataFlow, HDF

No data available

Overview

Cloudera DataFlow (CDF) is a comprehensive edge-to-cloud real-time streaming data platform that gathers, curates, and analyzes data to provide customers with useful insight for immediately actionable intelligence. It resolves issues with real-time stream processing, streaming analytics, data provenance, and data ingestion from IoT devices and other sources that are associated with data in motion. Cloudera DataFlow enables secure and controlled data intake, data transformation, and content routing because it is built entirely on open-source technologies. With regard to all of your strategic digital projects, Cloudera DataFlow enables you to provide a superior customer experience, increase operational effectiveness, and maintain a competitive edge.

With Cloudera DataFlow, you can take the next step in modernizing your data streams by connecting your on-premises flow management, streams messaging, and stream processing and analytics capabilities to the public cloud.

Cloudera DataFlow Advantage Features

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

Edge and flow management: Edge agents and an edge management hub work together to provide the edge management capability. Edge agents can be managed, controlled, and watched over in order to gather information from edge hardware and push intelligence back to the edge. Thousands of edge devices can now be used to design, deploy, run, and monitor edge flow apps. Edge Flow Manager (EFM) is an agent management hub that enables the development, deployment, and monitoring of edge flows on thousands of MiNiFi agents using a graphical flow-based programming model.

Streams messaging: The CDF platform guarantees that all ingested data streams can be temporarily buffered so that other applications can use the data as needed. This makes it possible for a business to scale efficiently, as data streams from thousands of origination points start to grow to petabyte sizes. To achieve IoT-scale, streams messaging allows you to buffer large data streams using a publish-subscribe strategy.

Stream analytics and processing: The third tenet of the CDF platform is its capacity to analyze incoming data streams in real time and with minimal latency, providing actionable intelligence in the form of predictive and prescriptive insights. This stage is essential to completing the Data-in-Motion lifecycle for an enterprise because there is only a use in absorbing all real-time streams if something useful is done with them in the moment to benefit your company.

Shared Data Experience (SDX): The most crucial component that transforms CDF into a genuine platform is Cloudera Data Platform's SDX. It is a powerful data fabric that offers the broadest possible deployment flexibility and guarantees total security, governance, and control across infrastructures. You get a single experience for security (with Apache Ranger), governance (with Apache Atlas), and data lineage from edge to cloud because all the CDF components seamlessly connect with SDX.

Cloudera DataFlow Advantage Benefits

There are many benefits to implementing Cloudera DataFlow . Some of the biggest advantages the solution offers include:

Completely open source: Invest in your architecture with confidence, knowing that there will be no vendor lock-in.

More than 300 pre-built processors: This is the only product that provides edge-to-cloud connection this comprehensive as well as a no-code user experience

Integrated data provenance: The market's only platform that offers out-of-the-box, end-to-end data lineage tracking and provenance across MiNiFi, NiFi, Kafka, Flink, and more.

Multiple stream processing engines to choose from: Supports Spark structured streaming, Kafka Streams, and Apache Flink for real-time insights and predictive analytics.

Hundred of Kafka consumers: Cloudera has hundreds of satisfied customers who receive exceptional support for their complex Kafka implementations.

Use cases for edge IoT: IoT data from thousands of endpoints may be easily collected, processed, and managed from the edge to the cloud with a multi-cloud/hybrid cloud strategy.

Hybrid/multi-cloud approach: Choose a flexible deployment option for your streaming architecture that spans across edge, on-premises, and various cloud environments with ease thanks to the power of CDP.

Cloudera

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

Clearsense

Information Not Available

Buyer's Guide

Cloudera DataFlow vs. Spring Cloud Data Flow

December 2025

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