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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
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 Confluent is 8.5%, down from 11.6% 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

Yantao Zhao - PeerSpot reviewer
Great tool for sharing knowledge, internal communication and allows for real-time collaboration on pages
Confluence is easy to use and modify. However, sometimes there are too many pages. We have to reorganize the folder or parent account. Since everyone can create a page, the same knowledge might be created in multiple places by different people. This leads to redundancy and makes it difficult to find information. It's not centralized. So it could be more user-friendly and centralized. A way to reduce redundancy would be helpful. It's very easy to use, so everyone can create knowledge. But it would be good to synchronize and organize that information a bit better. Another improvement would be in Confluence search. You can search for keywords, but it's not like AI, not even ChatGPT or OpenAI. It would be nice to get more relevant or organized answers. If you're outside the company, you just get some titles containing the keyword you input. But if Confluence were like a database, you could input something and get a well-organized search offering from multiple pages.
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 most valuable is its capability to enhance the documentation process, particularly when creating software documentation."
"Confluent facilitates the messaging tasks with Kafka, streamlining our processes effectively."
"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."
"Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance."
"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."
"We mostly use the solution's message queues and event-driven architecture."
"It is also good for knowledge base management."
"Their tech support is amazing; they are very good, both on and off-site."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"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."
"The support team is good and it's easy to use."
"I would rate the overall solution a ten out of ten."
"The service is relatively cheap compared to other batch-processing engines."
"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."
 

Cons

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."
"In Confluent, there could be a few more VPN options."
"The formatting aspect within the page can be improved and more powerful."
"It would help if the knowledge based documents in the support portal could be available for public use as well."
"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."
"Confluence could improve the server version of the solution. However, most companies are going to the cloud."
"Confluent has a good monitoring tool, but it's not customizable."
"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."
"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 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."
"Google Cloud Dataflow should include a little cost optimization."
"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 authentication part of the product is an area of concern where improvements are required."
"The solution's setup process could be more accessible."
"The technical support has slight room for improvement."
 

Pricing and Cost Advice

"Confluent is an expensive solution."
"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."
"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."
"It comes with a high cost."
"Confluent is highly priced."
"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."
"Google Cloud is slightly cheaper than AWS."
"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 solution is cost-effective."
"The solution is not very expensive."
"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 the solution's pricing a seven to eight out of ten."
"The tool is cheap."
"Google Cloud Dataflow is a cheap solution."
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
17%
Manufacturing Company
8%
Insurance Company
5%
Financial Services Firm
17%
Retailer
12%
Manufacturing Company
12%
Computer Software Company
12%
 

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?
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 maintenanc...
What needs improvement with Confluent?
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 c...
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...
 

Comparisons

 

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: January 2025.
837,501 professionals have used our research since 2012.