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Apache Kafka on Confluent Cloud vs Cloudera 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 Kafka on Confluent C...
Ranking in Streaming Analytics
13th
Average Rating
8.6
Reviews Sentiment
5.6
Number of Reviews
15
Ranking in other categories
No ranking in other categories
Cloudera DataFlow
Ranking in Streaming Analytics
19th
Average Rating
7.4
Reviews Sentiment
6.5
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2026, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.9%, up from 0.1% compared to the previous year. The mindshare of Cloudera DataFlow is 2.1%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Apache Kafka on Confluent Cloud0.9%
Cloudera DataFlow2.1%
Other97.0%
Streaming Analytics
 

Featured Reviews

AF
Lead Software Engineer at a tech vendor with 10,001+ employees
Has unified log streams from multiple systems and accelerated issue tracking through streamlined setup
I think Apache Kafka on Confluent Cloud can be improved by probably working more around Confluent or the tool. In my opinion, it should utilize the response structures in a better way or be able to detect if there is any variable or if there is any data structure that is mismatched, as it would be easier than us manually having to put in the exact name in order for it to match the response. Regarding additional improvements, I would say probably around error handling, where when we encounter errors specific to our response structures and everything, or the tables or anything of that nature, it would be better if we were prompted with better error handling mechanisms. I do not think there are any other improvements Apache Kafka on Confluent Cloud needs, aside from error handling and response structures.
Mohamed-Saied - PeerSpot reviewer
Senior Data Architect at Teradata Corporation
Efficient data integration and workflow scheduling elevate project performance
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and…

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use."
"It's very fast and helps us to create the project, guarantee the message delivery, and the performance."
"The benefits that I have seen from having a real-time architecture include better velocity for developers; instead of developing many of those capabilities in each team, we can rely on Apache Kafka on Confluent Cloud to provide those functionalities we want, and the teams can focus on their own business instead of providing all sorts of APIs and dependencies to other domains, allowing everyone to run faster."
"Apache Kafka on Confluent Cloud is more reliable and frequent to use compared to Apache Kafka."
"The order guarantee of Apache Kafka on Confluent Cloud and the amount of throughput it can handle are valuable; the fact that the consumer pulls the data, not the broker, makes it more resilient and more reliable compared to other technologies."
"Kafka and Confluent Cloud have proven to be cost-effective, especially when compared to other tools. In a recent BI integration program over the past year, we assessed multiple use cases spanning ship-to-shore and various Azure integrations. Our findings revealed that Confluent Kafka performed exceptionally well, standing out alongside Genesys and Azure Event Hubs. While these three are top contenders, the choice among other tools depends on the specific use case and project requirements. The customer initially used tools like SMQs, FITRA, and Stream for real-time data processing. However, after our recommendation, Confluent Cloud proved to be a superior choice, capable of replacing these three tools and simplifying their data infrastructure. This shift to a single tool, Confluent Cloud, streamlined their operations, making maintenance and management more efficient for their internal projects."
"Overall, I think it's a good experience. Apache Kafka can be quite complex and difficult to maintain on your own, so using Apache Kafka on Confluent Cloud makes it much easier to use it without worrying about setup and maintenance."
"Confluent helped me to streamline all those logs into one place, and then I was consuming those logs that were produced, which made it very much easier because I know Kafka and using Confluent made it much simpler."
"The most effective features are data management and analytics."
"The initial setup was not so difficult"
"This solution is very scalable and robust."
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"DataFlow's performance is okay."
 

Cons

"The ability to implement request-response communication on Apache Kafka needs improvement."
"In terms of improvements, observability and monitoring are areas that could be enhanced. They are lacking in terms of observability and monitoring compared to other products."
"Improvement can be made by making it easier to build applications on the real-time stream, focusing on real-time pre-processing and anomaly detection."
"Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges."
"There could be an in-built feature for data analysis."
"There's one thing that's a common use case, but I don't know why it's not covered in Kafka. When a message comes in, and another message with the same key arrives, the first version should be deleted automatically."
"I thought Confluent would stop me when I crossed the credits, but it did not, and then I got charged."
"Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods."
"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
 

Pricing and Cost Advice

"I consider that the product's price falls under the middle range category."
"I think the pricing is fair, but Confluent requires a little bit more thinking because the price can go up really quickly when it comes to premium connectors."
"Regarding pricing, Apache Kafka on Confluent Cloud is not a cheap tool. The right use case would justify the cost. It might make sense if you have a high volume of data that you can leverage to generate value for the business. But if you don't have those requirements, there are likely cheaper solutions you could use instead."
"DataFlow isn't expensive, but its value for money isn't great."
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Top Industries

By visitors reading reviews
Construction Company
16%
Financial Services Firm
14%
Manufacturing Company
8%
Comms Service Provider
7%
Financial Services Firm
18%
Construction Company
14%
Manufacturing Company
10%
Comms Service Provider
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Midsize Enterprise3
Large Enterprise8
No data available
 

Questions from the Community

What needs improvement with Apache Kafka on Confluent Cloud?
I think Apache Kafka on Confluent Cloud can be improved by probably working more around Confluent or the tool. In my opinion, it should utilize the response structures in a better way or be able to...
What is your primary use case for Apache Kafka on Confluent Cloud?
I have used Apache Kafka on Confluent Cloud for one of my projects with regard to log monitoring. My main use case for Apache Kafka on Confluent Cloud in that project was mainly streaming of the lo...
What advice do you have for others considering Apache Kafka on Confluent Cloud?
My advice to others looking into using Apache Kafka on Confluent Cloud is that it is easier and has a low learning curve. If there is any use case regarding streaming, I would suggest starting off ...
What needs improvement with Cloudera DataFlow?
Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.
What is your primary use case for Cloudera DataFlow?
Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily...
What advice do you have for others considering Cloudera DataFlow?
Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems. However, the learning curve is high, and there is a shor...
 

Also Known As

No data available
CDF, Hortonworks DataFlow, HDF
 

Overview

 

Sample Customers

Information Not Available
Clearsense
Find out what your peers are saying about Apache Kafka on Confluent Cloud vs. Cloudera DataFlow and other solutions. Updated: June 2026.
902,988 professionals have used our research since 2012.