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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
19th
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
8th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of February 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.0%, up from 1.9% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.7%, up from 6.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

CB
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
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."
"The solution allows us to program in any language we desire."
"The support team is good and it's easy to use."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"It is a scalable solution."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"The service is relatively cheap compared to other batch-processing engines."
 

Cons

"Documentation is poor because much of it is in Chinese with no English translation."
"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."
"Promoting the technology more broadly would help increase its adoption."
"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."
"The technical support has slight room for improvement."
"Google Cloud Dataflow should include a little cost optimization."
"The solution's setup process could be more accessible."
"They should do a market survey and then make improvements."
 

Pricing and Cost Advice

Information not available
"Google Cloud Dataflow is a cheap solution."
"The solution is not very expensive."
"Google Cloud is slightly cheaper than AWS."
"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 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 tool is cheap."
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Top Industries

By visitors reading reviews
Computer Software Company
20%
Financial Services Firm
10%
Government
8%
Manufacturing Company
8%
Financial Services Firm
17%
Retailer
12%
Manufacturing Company
12%
Computer Software Company
12%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

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 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...
 

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), Microsoft and others in Streaming Analytics. Updated: January 2025.
837,501 professionals have used our research since 2012.