Apache Spark Streaming vs Spring Cloud Data Flow comparison

Apache and VMware are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while VMware is ranked #9 with an average rating of 7.7. Apache holds a 3.1% mindshare in SA, compared to VMware’s 4.9% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

1,992 Views
1,481 Comparison Views

91% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,349 Views
1,758 Comparison Views

88% willing to recommend

Apache Spark Streaming

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 17, 2024

Spring Cloud Data Flow and Apache Spark Streaming compete in the data streaming and processing domain. Based on the comparisons, Apache Spark Streaming seems to have the upper hand due to its robust scalability and processing power.

Features: Spring Cloud Data Flow supports stream processing, task scheduling, and integration with Spring Boot, making it suitable for microservices environments. Apache Spark Streaming offers high throughput, real-time processing, and machine learning capabilities, ideal for handling large-scale data. Spring Cloud focuses on modularity while Spark emphasizes speed and scalability.

Room for Improvement: Spring Cloud Data Flow could enhance its graphical visualization tools, provide more off-the-shelf components, and improve custom component development. Apache Spark Streaming could benefit from easier setup procedures, more intuitive configuration options, and better integration aids without needing extensive technical expertise.

Ease of Deployment and Customer Service: Spring Cloud Data Flow is praised for its simple setup and strong customer support, making it favorable for businesses needing quick deployment and integration. Apache Spark Streaming, while complex, offers comprehensive resources and community support, though it demands higher technical competence for configuration.

Pricing and ROI: Spring Cloud Data Flow is cost-effective with lower initial costs, delivering solid ROI thanks to efficient integrations. Apache Spark Streaming requires higher setup costs but offers substantial ROI for extensive data operations, leveraging its processing power for environments with large-scale data processing needs.

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

Buyer's Guide

Apache Spark Streaming 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

Apache Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

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 (22nd)

Mindshare comparison

As of February 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.1%, down from 3.9% 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

AbhishekGupta

Engineering Leader at Walmart

Easy integration, beneficial auto-scaling, and good open-sourced support community

The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better. Apache Spark Streaming does not have auto-tuning. A customer needs to invest a lot, in terms of management and maintenance.

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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 Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"The solution is very stable and reliable."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"It's the fastest solution on the market with low latency data on data transformations."

More Apache Spark Streaming pros

"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 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 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 product is very user-friendly."

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

Cons

"We would like to have the ability to do arbitrary stateful functions in Python."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"The solution itself could be easier to use."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"In terms of improvement, the UI could be better."

"The debugging aspect could use some improvement."

"Integrating event-level streaming capabilities could be beneficial."

More Apache Spark Streaming cons

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

"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

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

"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."

"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."

Pricing and Cost Advice

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"People pay for Apache Spark Streaming as a service."

"I was using the open-source community version, which was self-hosted."

"Spark is an affordable solution, especially considering its open-source nature."

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

20%

Manufacturing Company

University

Financial Services Firm

26%

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 Apache Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

See all answers

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

See all answers

What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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

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Streaming Analytics

January 2025

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Buyer's Guide

Spring Cloud Data Flow

January 2025

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Also Known As

Spark Streaming

No data available

Overview

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

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.

VMware

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Information Not Available

Buyer's Guide

Apache Spark Streaming vs. Spring Cloud Data Flow

January 2025

Free Report: Apache Spark Streaming vs. Spring Cloud Data Flow

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