Apache Spark Streaming vs Spring Cloud Data Flow comparison

Why PeerSpot?

Apache and Broadcom are both solutions in the Streaming Analytics category. Apache is ranked #8 with an average rating of 7.9, while Broadcom is ranked #18 with an average rating of 7.5. Apache holds a 4.6% mindshare in SA, compared to Broadcom’s 2.5% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,888 Views
2,888 Comparison Views

94% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

4,715 Views
1,509 Comparison Views

88% willing to recommend

Apache Spark Streaming

Spring Cloud Data Flow

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Spring Cloud Data Flow

September 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 Spark Streaming

Ranking in Streaming Analytics

8th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

18th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (33rd)

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.6%, up from 3.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 2.5%, down from 4.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.6%
Spring Cloud Data Flow	2.5%
Other	92.9%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

"It is the most scalable tool that I have seen before."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

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

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

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

"The solution is better than average and some of the valuable features include efficiency and stability."

More Apache Spark Streaming pros

"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 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."

"Overall, Spring Cloud Data Flow is a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon."

"The product is very user-friendly."

"This product will assist us in saving costs in many ways: No longer need to continue paying high fees for proprietary software, reduce the number of software engineers needed to support the product, and achieve faster time to market by using this product for our middleware."

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

"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."

More Spring Cloud Data Flow pros

Cons

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

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

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

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

"The downside is when you have this the other way around in the columns, it becomes really hard 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."

"The solution itself could be easier to use."

More Apache Spark Streaming cons

"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."

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

"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 visual user interface could use some help; it needs improvement."

"The documentation on offer is not that good."

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

More Spring Cloud Data Flow cons

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."

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

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

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

"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

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare Company

Financial Services Firm

16%

Computer Software Company

10%

Outsourcing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

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

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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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 Spark Streaming

September 2026

Download Apache Spark Streaming product report

Buyer's Guide

Spring Cloud Data Flow

August 2026

Download Spring Cloud Data Flow product report

Also Known As

Spark Streaming

No data available

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

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

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

Information Not Available

Buyer's Guide

Apache Spark Streaming vs. Spring Cloud Data Flow

September 2026

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

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