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Confluent vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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

Confluent
Ranking in Streaming Analytics
4th
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
23
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
7th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Confluent is 8.3%, down from 10.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 6.5%, down from 7.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Gustavo-Barbosa Dos Santos - PeerSpot reviewer
Has good technical support services and a valuable feature for real-time data streaming
Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance. It helps us understand the various requirements of multiple customers and validates the information for different versions. We can automate the tasks using Confluent Kafka. Thus, it guarantees us the data quality and maintains the integrity of message contracts.
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 monitoring module is impressive."
"It is also good for knowledge base management."
"The solution can handle a high volume of data because it works and scales well."
"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."
"The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category."
"Their tech support is amazing; they are very good, both on and off-site."
"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."
"The client APIs are the most valuable feature."
"Google's support team is good at resolving issues, especially with large data."
"I would rate the overall solution a ten out of ten."
"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."
"The support team is good and it's easy to use."
"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 most valuable features of Google Cloud Dataflow are scalability and connectivity."
"Google Cloud Dataflow is useful for streaming and data pipelines."
 

Cons

"Confluence could improve the server version of the solution. However, most companies are going to the cloud."
"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."
"There is a limitation when it comes to seamlessly importing Microsoft documents into Confluent pages, which can be inconvenient for users who frequently work with Microsoft Office tools and need to transition their content to Confluent."
"Currently, in the early stages, I see a gap on the security side. If you are using the SaaS version, we would like to get a fuller, more secure solution that can be adopted right out of the box. Confluence could do a better job sharing best practices or a reusable pattern that others have used, especially for companies that can not afford to hire professional services from Confluent."
"In Confluent, there could be a few more VPN options."
"One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it challenging to manage access at scale if we have many different data sources."
"It requires some application specific connectors which are lacking. This needs to be added."
"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."
"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."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"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."
"Promoting the technology more broadly would help increase its adoption."
"The solution's setup process could be more accessible."
"The deployment time could also be reduced."
 

Pricing and Cost Advice

"Confluent is highly priced."
"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."
"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."
"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."
"The solution is cheaper than other products."
"Confluent is an expensive solution as we went for a three contract and it was very costly for us."
"Confluent has a yearly license, which is a bit high because it's on a per-user basis."
"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."
"Google Cloud Dataflow is a cheap solution."
"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."
"Google Cloud is slightly cheaper than AWS."
"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."
"The solution is not very expensive."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
18%
Computer Software Company
16%
Manufacturing Company
6%
Insurance Company
5%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Confluent?
I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...
What is your experience regarding pricing and costs for Confluent?
They charge a lot for scaling, which makes it expensive.
What needs improvement with Confluent?
I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team. The lack of easy access to the Confluent support team is also a...
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?
I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...
 

Also Known As

No data available
Google Dataflow
 

Overview

 

Sample Customers

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Confluent vs. Google Cloud Dataflow and other solutions. Updated: July 2025.
862,624 professionals have used our research since 2012.