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Apache Pulsar vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive Summary

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
21st
Average Rating
8.0
Reviews Sentiment
6.2
Number of Reviews
1
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
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2026, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.7%, up from 2.0% compared to the previous year. The mindshare of Google Cloud Dataflow is 4.2%, down from 7.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Google Cloud Dataflow4.2%
Apache Pulsar2.7%
Other93.1%
Streaming Analytics
 

Featured Reviews

it_user1087029 - PeerSpot reviewer
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.
Jana Polianskaja - PeerSpot reviewer
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.

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."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"The support team is good and it's easy to use."
"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."
"Google's support team is good at resolving issues, especially with large data."
"It is a scalable solution."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
 

Cons

"Documentation is poor because much of it is in Chinese with no English translation."
"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."
"The solution's setup process could be more accessible."
"Promoting the technology more broadly would help increase its adoption."
"Google Cloud Dataflow should include a little cost optimization."
"The authentication part of the product is an area of concern where improvements are required."
"The deployment time could also be reduced."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
 

Pricing and Cost Advice

Information not available
"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."
"Google Cloud is slightly cheaper than AWS."
"The solution is not very expensive."
"The solution is cost-effective."
"The tool is cheap."
"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."
"Google Cloud Dataflow is a cheap solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
10%
Insurance Company
8%
Comms Service Provider
8%
Financial Services Firm
18%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise10
 

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.
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.
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...
 

Also Known As

No data available
Google Dataflow
 

Overview

 

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), Apache and others in Streaming Analytics. Updated: January 2026.
881,707 professionals have used our research since 2012.