Apache Spark Streaming vs Spring Cloud Data Flow comparison

Apache and VMware are both solutions in the Streaming Analytics category. Apache is ranked #9 with an average rating of 8.0, while VMware is ranked #11 with an average rating of 7.8. Apache holds a 3.5% mindshare in SA, compared to VMware’s 4.4% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 85% of VMware users who would recommend it.

Apache Spark Streaming

Read 10 Apache Spark Streaming reviews

3,122 Views
2,368 Comparison Views

90% willing to recommend

Spring Cloud Data Flow

Read 7 Spring Cloud Data Flow reviews

3,100 Views
2,326 Comparison Views

85% willing to recommend

Apache Spark Streaming

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark Streaming and Spring Cloud Data Flow based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Spark Streaming vs. Spring Cloud Data Flow Report (Updated: July 2024).

Buyer's Guide

Apache Spark Streaming vs. Spring Cloud Data Flow

July 2024

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Categories and Ranking

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

8.0

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

Number of Reviews

Ranking in other categories

Data Integration (28th)

Mindshare comparison

As of September 2024, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.5%, down from 4.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.4%, up from 4.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

RajeevKumar10

DevOps engineer at Vvolve management consultants

Jun 3, 2024

Handles large datasets and is relatively easy to manage, especially with cloud technologies but scalability features could be enhanced

I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast and easy. In my ETL work, I often move data from multiple sources into a data lake. Apache Spark is…

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Juzer Ali

Software engineer at Codinglama

Apr 4, 2023

Simple programming model, low maintenance, but interface could improve

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. When we are using Docker images as the applications, the UI does not work, or it is not clear how it works. In a feature release, it would be beneficial if there could be more supported languages. They only support Spring, Node.js, and Python.

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

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

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

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

"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."

"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."

"As an open-source solution, using it is basically free."

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."

"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 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."

"The product is very user-friendly."

Cons

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

"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."

"It was resource-intensive, even for small-scale applications."

"The solution itself could be easier to use."

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

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

"The initial setup is quite complex."

More Apache Spark Streaming 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."

"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 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."

Pricing and Cost Advice

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

"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."

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

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

"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

23%

Computer Software Company

20%

Manufacturing Company

University

Financial Services Firm

28%

Computer Software Company

17%

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?

The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of exp...

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What is your primary use case for Apache Spark Streaming?

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What needs improvement with Spring Cloud Data Flow?

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 ...

See all answers

What is your primary use case for Spring Cloud Data Flow?

I work with a leading company in the recruitment domain in India. When a job seeker applies for a job, we capture the intent. It has to flow by a certain pipeline to check all the scores and whethe...

See all answers

What advice do you have for others considering Spring Cloud Data Flow?

Our experience with Spring Cloud Data Flow has been phenomenal. The best thing I like about it is you can plug and play any data source, including MongoDB, Elasticsearch, and MySQL. We had a use ca...

See all answers

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Compared 9% of the time

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Compared 7% of the time

Databricks vs Spring Cloud Data Flow

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Product Reports

Buyer's Guide

Streaming Analytics

August 2024

Download Apache Spark Streaming product report

Buyer's Guide

Spring Cloud Data Flow

August 2024

Download Spring Cloud Data Flow product report

Also Known As

Spark Streaming

No data available

Learn More

Apache

VMware

Overview

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

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.

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

July 2024

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

Find out what your peers are saying about Apache Spark Streaming vs. Spring Cloud Data Flow and other solutions. Updated: July 2024.

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