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Cloudera DataFlow 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

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
Google Cloud Dataflow
Ranking in Streaming Analytics
10th
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
14
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Cloudera DataFlow is 1.6%, up from 1.2% compared to the previous year. The mindshare of Google Cloud Dataflow is 4.5%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Google Cloud Dataflow4.5%
Cloudera DataFlow1.6%
Other93.9%
Streaming Analytics
 

Featured Reviews

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…
Jana Polianskaja - PeerSpot reviewer
Data Engineer at Accenture
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 effective features are data management and analytics."
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"The initial setup was not so difficult"
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The integration within Google Cloud Platform is very good."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"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 best feature of Google Cloud Dataflow is its practical connectedness."
 

Cons

"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."
"Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."
"The deployment time could also be reduced."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"The solution's setup process could be more accessible."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"They should do a market survey and then make improvements."
"Promoting the technology more broadly would help increase its adoption."
 

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."
"The solution is cost-effective."
"Google Cloud Dataflow is a cheap solution."
"Google Cloud is slightly cheaper than AWS."
"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."
"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."
"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."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
19%
Manufacturing Company
12%
Retailer
11%
Healthcare Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise10
 

Questions from the Community

What do you like most about Cloudera DataFlow?
The most effective features are data management and analytics.
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 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?
It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...
 

Also Known As

CDF, Hortonworks DataFlow, HDF
Google Dataflow
 

Overview

 

Sample Customers

Clearsense
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Cloudera DataFlow vs. Google Cloud Dataflow and other solutions. Updated: December 2025.
879,477 professionals have used our research since 2012.