Google Cloud Dataflow vs Spring Cloud Data Flow comparison

Google and VMware are both solutions in the Streaming Analytics category. Google is ranked #8 with an average rating of 7.7, while VMware is ranked #10 with an average rating of 7.6. Google holds a 8.3% mindshare in SA, compared to VMware’s 5.0% mindshare. Additionally, 90% of Google users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Google Cloud Dataflow

Read 10 Google Cloud Dataflow reviews

4,001 Views
3,284 Comparison Views

90% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

2,743 Views
2,051 Comparison Views

88% willing to recommend

Google Cloud Dataflow

Spring Cloud Data Flow

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Executive Summary

We performed a comparison between Google Cloud Dataflow 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 Google Cloud Dataflow vs. Spring Cloud Data Flow Report (Updated: October 2024).

Buyer's Guide

Google Cloud Dataflow vs. Spring Cloud Data Flow

October 2024

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Categories and Ranking

Google Cloud Dataflow

Ranking in Streaming Analytics

8th

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Spring Cloud Data Flow

Ranking in Streaming Analytics

10th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Integration (22nd)

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Google Cloud Dataflow is 8.3%, up from 6.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 5.0%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Tamer Talal

Satellite System Engineer at NARSS

A tool useful for data transmission and data storage that needs to improve its authentication area

The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the product is an area of concern where improvements are required. In the future, the product should be made available at a cheaper rate.

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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

"The support team is good and it's easy to use."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"The product's installation process is easy...The tool's maintenance part is somewhat easy."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The service is relatively cheap compared to other batch-processing engines."

"It is a scalable solution."

"Google Cloud Dataflow is useful for streaming and data pipelines."

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

More Google Cloud Dataflow pros

"The product is very user-friendly."

"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 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 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 most valuable feature is real-time streaming."

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

Cons

"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

"The deployment time could also be reduced."

"They should do a market survey and then make improvements."

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

"The solution's setup process could be more accessible."

"Google Cloud Dataflow should include a little cost optimization."

"The authentication part of the product is an area of concern where improvements are required."

More Google Cloud Dataflow cons

"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."

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

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

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

"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 cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"The solution is cost-effective."

"Google Cloud Dataflow is a cheap solution."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"The solution is not very expensive."

"Google Cloud is slightly cheaper than AWS."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"The tool is cheap."

"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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

12%

Computer Software Company

11%

Manufacturing Company

11%

Financial Services Firm

29%

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 Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

See all answers

What is your experience regarding pricing and costs for Google Cloud Dataflow?

The solution is not very expensive.

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What needs improvement with Google Cloud Dataflow?

See all answers

What needs improvement with Spring Cloud Data Flow?

I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spr...

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

I am a developer using Spring Cloud Dataflow. We primarily use it to convert our applications from monolithic to microservices. The solution is used for scheduling tasks in a specific order and ens...

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

My advice would be to thoroughly review the documentation and understand if Spring Cloud Dataflow is the right solution for your application. For applications with only one or two microservices, it...

See all answers

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Streaming Analytics

November 2024

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Buyer's Guide

Spring Cloud Data Flow

October 2024

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Also Known As

Google Dataflow

No data available

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Google

VMware

Overview

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

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

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Information Not Available

Buyer's Guide

Google Cloud Dataflow vs. Spring Cloud Data Flow

October 2024

Free Report: Google Cloud Dataflow vs. Spring Cloud Data Flow

Find out what your peers are saying about Google Cloud Dataflow vs. Spring Cloud Data Flow and other solutions. Updated: October 2024.

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