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 7.9, while VMware is ranked #9 with an average rating of 7.7. Google holds a 7.7% mindshare in SA, compared to VMware’s 4.9% mindshare. Additionally, 91% of Google users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Google Cloud Dataflow

Read 11 Google Cloud Dataflow reviews

3,450 Views
2,838 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

Google Cloud Dataflow

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Spring Cloud Data Flow compete in the data processing sector, each with its strengths. Google Cloud Dataflow holds an advantage with its comprehensive ecosystem support and seamless deployments, while Spring Cloud Data Flow's orchestration capabilities provide greater flexibility for microservices.

Features: Google Cloud Dataflow is known for its integration with Google's managed services, scalability, and real-time stream and batch processing capabilities. Spring Cloud Data Flow excels in orchestration for microservices, supports advanced streaming and batch processing, and integrates effectively with Spring's ecosystem.

Room for Improvement: Google Cloud Dataflow could improve by offering more flexible platform options and enhanced community-driven support. It might also benefit from more user-friendly documentation for non-Google services. Spring Cloud Data Flow could work on streamlining its initial setup process, expanding its official customer support, and optimizing performance in smaller-scale applications.

Ease of Deployment and Customer Service: Google Cloud Dataflow offers easy deployment through managed services with strong Google Cloud integration and extensive customer support. Spring Cloud Data Flow provides greater deployment flexibility across different platforms like Kubernetes, despite requiring more setup time and having limited support.

Pricing and ROI: Google Cloud Dataflow offers a pay-as-you-go pricing model, ideal for scalable needs with positive ROI for dynamic workloads. Its integration with Google Cloud aids in cost management. Spring Cloud Data Flow, while more expensive initially, delivers long-term ROI through efficient application integrations and reduced time-to-market, ideal for enterprises seeking feature-rich solutions.

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

Buyer's Guide

Google Cloud Dataflow 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

Google Cloud Dataflow

Ranking in Streaming Analytics

8th

Average Rating

8.0

Reviews Sentiment

7.3

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 Google Cloud Dataflow is 7.7%, up from 6.8% 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

Jana Polianskaja

Data Engineer at Accenture

Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow

As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.

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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 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."

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

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

"The best feature of Google Cloud Dataflow is its practical connectedness."

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

"The solution allows us to program in any language we desire."

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

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

More Google Cloud Dataflow pros

"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 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 dashboards in Spring Cloud Dataflow are quite valuable."

"The product is very user-friendly."

"The most valuable feature is real-time streaming."

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

"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."

Cons

"The deployment time could also be reduced."

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

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

"There are certain challenges regarding the Google Cloud Composer which can be improved."

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

"The technical support has slight room for improvement."

"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."

"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."

More Google Cloud Dataflow cons

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

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

"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."

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

"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 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 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

"Google Cloud is slightly cheaper than AWS."

"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."

"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 is cost-effective."

"The solution is not very expensive."

"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 provides value for money, and we are currently using its community edition."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

12%

Manufacturing Company

12%

Computer Software Company

12%

Financial Services Firm

25%

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 Google Cloud Dataflow?

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

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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?

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 p...

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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

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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

Google Dataflow

No data available

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.

Google

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

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

January 2025

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: January 2025.

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