Apache Pulsar vs Google Cloud Dataflow comparison

Pulsar and Google are both solutions in the Streaming Analytics category. Pulsar is ranked #22, while Google is ranked #9 with an average rating of 8.1. Pulsar holds a 2.5% mindshare in SA, compared to Google’s 4.6% mindshare. Additionally, 100% of Pulsar users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Apache Pulsar

Read 1 Apache Pulsar review

680 Views
680 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,251 Views
1,891 Comparison Views

93% willing to recommend

Apache Pulsar

Google Cloud Dataflow

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Executive Summary

We performed a comparison between Apache Pulsar and Google Cloud Dataflow based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: December 2025).

Buyer's Guide

Streaming Analytics

December 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

Apache Pulsar

Ranking in Streaming Analytics

22nd

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Cloud Dataflow

Ranking in Streaming Analytics

9th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.5%, up from 1.9% compared to the previous year. The mindshare of Google Cloud Dataflow is 4.6%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Google Cloud Dataflow	4.6%
Apache Pulsar	2.5%
Other	92.9%

Streaming Analytics

Featured Reviews

it_user1087029

Solution Architect at Vlaanderen connect.

The solution can mimic other APIs without changing a line of code

The solution operates as a classic message broker but also as a streaming platform. It operates differently than a traditional streaming platform with storage and computing handled separately. It scales easier and better than Kafka which can be stubborn. You can even make it act like Kafka because it understands Kafka APIs. There are even companies that will sell you Kafka but underneath it is Apache Pulsar. The solution is very compatible because it can mimic other APIs without changing a line of code.

Read full review

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.

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

"The solution operates as a classic message broker but also as a streaming platform."

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

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

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

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

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

"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."

"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

Cons

"Documentation is poor because much of it is in Chinese with no English translation."

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

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

"The deployment time could also be reduced."

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

"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."

"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."

"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."

More Google Cloud Dataflow cons

Pricing and Cost Advice

Information not available

"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."

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

"The solution is cost-effective."

"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 is not very expensive."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

15%

Comms Service Provider

Government

Financial Services Firm

19%

Manufacturing Company

12%

Retailer

11%

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	10

Questions from the Community

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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?

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

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What needs improvement with Google Cloud Dataflow?

It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...

See all answers

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Product Reports

Buyer's Guide

Streaming Analytics

December 2025

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Buyer's Guide

Google Cloud Dataflow

December 2025

Download Google Cloud Dataflow product report

Also Known As

No data available

Google Dataflow

Overview

Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project

Pulsar

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

Sample Customers

Information Not Available

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

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics. Updated: December 2025.

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