Google Cloud Dataflow vs Spring Cloud Data Flow comparison

Why PeerSpot?

Google and Broadcom are both solutions in the Streaming Analytics category. Google is ranked #13 with an average rating of 8.4, while Broadcom is ranked #18 with an average rating of 7.5. Google holds a 3.4% mindshare in SA, compared to Broadcom’s 2.5% mindshare. Additionally, 93% of Google users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Google Cloud Dataflow

Read 15 Google Cloud Dataflow reviews

3,125 Views
2,688 Comparison Views

93% 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

Google Cloud Dataflow

Spring Cloud Data Flow

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Google Cloud Dataflow 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

Google Cloud Dataflow

Ranking in Streaming Analytics

13th

Average Rating

8.0

Reviews Sentiment

6.8

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 Google Cloud Dataflow is 3.4%, down from 5.4% 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 (%)
Google Cloud Dataflow	3.4%
Spring Cloud Data Flow	2.5%
Other	94.1%

Streaming Analytics

Featured Reviews

Mohammed Aaqibuddin

Senior Cdp Specialist at DigitasLBi

Unified user personas have improved data workflows and support detailed monitoring and logging

Google Cloud has many streams and products. In Google Cloud, everything is translated in the backend, so we do not have to use services such as Apache Beam. When you want to use Google Cloud Functions, you write the code, and the backend talks to all the libraries or Apache, so we do not need to be concerned about those. We just need to use our functions that translate and have many tools and services readily available. Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment. For example, if I am using Google Cloud Functions, I can easily see what changes I have done and trace it properly. I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."

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

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

"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 integration within Google Cloud Platform is very good."

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

"It is a scalable solution."

More Google Cloud Dataflow pros

"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."

"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."

"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."

More Spring Cloud Data Flow pros

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."

"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."

"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."

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

"Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company."

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

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."

More Google Cloud Dataflow cons

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

"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."

"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."

"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."

"The documentation on offer is not that good."

More Spring Cloud Data Flow cons

Pricing and Cost Advice

"The tool is cheap."

"Google Cloud Dataflow is a cheap solution."

"Google Cloud is slightly cheaper than AWS."

"The solution is cost-effective."

"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."

"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."

"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

19%

Manufacturing Company

12%

Comms Service Provider

Construction 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	3
Midsize Enterprise	2
Large Enterprise	12

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

Questions from the Community

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

Pricing is normal. It is part of a package received from Google, and they are not charging us too high.

See all answers

What needs improvement with Google Cloud Dataflow?

I feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that man...

See all answers

What is your primary use case for Google Cloud Dataflow?

The primary use case for Google Cloud Dataflow is when a brand has a lot of data and wants to store it in their warehouse. They can use BigQuery to store their data or use big data solutions to sto...

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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

Google Cloud Dataflow

September 2026

Download Google Cloud Dataflow product report

Buyer's Guide

Spring Cloud Data Flow

August 2026

Download Spring Cloud Data Flow product report

Also Known As

Google Dataflow

No data available

Overview

Google Cloud Dataflow provides scalable batch and streaming data processing with Apache Beam integration, supporting Python and Java. It's designed for efficient data transformations, analytics, and machine learning, featuring cost-effective serverless operations.

Google Cloud Dataflow is a robust tool for handling large-scale data processing tasks with flexibility in processing batch and streaming workloads. It integrates seamlessly with other Google Cloud services like Pub/Sub for real-time messaging and BigQuery for advanced analytics. The platform supports a wide array of data transformation and preparation needs, making it suitable for complex data workflows and machine learning applications. Despite its advantages, users have noted challenges such as incomplete error logs, longer job startup times, and some limitations in the Python SDK.

What are the key features of Google Cloud Dataflow?

Apache Beam Integration: Allows for advanced data processing capabilities with extensive library support.
Flexible Language Support: Works seamlessly with Python and Java for diverse application requirements.
Scalable Processing: Manages both batch and streaming data efficiently to meet varying data loads.
Cost-Effective Model: Operates on a pay-as-you-go basis, optimizing resource expenditure.
Monitoring Tools: Provides comprehensive assessments to enhance pipeline performance.

What benefits do users experience with Google Cloud Dataflow?

Real-Time Analytics: Facilitates timely data insights essential for fast decision-making.
Integrated Ecosystem: Simplifies orchestration with services like Cloud Composer, enhancing workflow connectivity.
Data Transformation: Enhances machine learning models preparation with robust data cleansing capabilities.

Industries, especially in retail and eCommerce, implement Google Cloud Dataflow for effective batch job execution, data transformation, and event stream processing. It aids in constructing distributed data pipelines for handling extensive analytics tasks, supporting effective large-scale data-driven decisions.

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.

Broadcom

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

September 2026

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

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