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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
18th
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
7th
Average Rating
7.8
Reviews Sentiment
7.3
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.0%, up from 1.8% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

it_user1087029 - PeerSpot reviewer
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."
"The support team is good and it's easy to use."
"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 service is relatively cheap compared to other batch-processing engines."
"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."
"It is a scalable solution."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The integration within Google Cloud Platform is very good."
"I would rate the overall solution a ten out of ten."
 

Cons

"Documentation is poor because much of it is in Chinese with no English translation."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"The deployment time could also be reduced."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"They should do a market survey and then make improvements."
"The solution's setup process could be more accessible."
"The authentication part of the product is an area of concern where improvements are required."
"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."
 

Pricing and Cost Advice

Information not available
"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."
"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."
"Google Cloud Dataflow is a cheap solution."
"Google Cloud is slightly cheaper than AWS."
"The solution is cost-effective."
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Top Industries

By visitors reading reviews
Computer Software Company
20%
Government
8%
Financial Services Firm
8%
Manufacturing Company
7%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
11%
 

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 is your experience regarding pricing and costs for Google Cloud Dataflow?
Google Cloud Dataflow costs are primarily driven by compute resources (worker type and count) and data volume. However, other factors like pipeline complexity also contribute significantly to the t...
What needs improvement with Google Cloud Dataflow?
Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, ...
 

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: March 2025.
842,767 professionals have used our research since 2012.