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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
9th
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
15
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2026, in the Streaming Analytics category, the mindshare of Cloudera DataFlow is 1.9%, up from 0.9% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.9%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Google Cloud Dataflow3.9%
Cloudera DataFlow1.9%
Other94.2%
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…
reviewer2812851 - PeerSpot reviewer
Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees
Unified user personas have improved data workflows and support detailed monitoring and logging
Google Cloud has many streams and products. In Google Cloud, everything is translated in the backend, so we do not have to use services such as Apache Beam. When you want to use Google Cloud Functions, you write the code, and the backend talks to all the libraries or Apache, so we do not need to be concerned about those. We just need to use our functions that translate and have many tools and services readily available. Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment. For example, if I am using Google Cloud Functions, I can easily see what changes I have done and trace it properly. I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.

Quotes from Members

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

Pros

"This solution is very scalable and robust."
"The most effective features are data management and analytics."
"The initial setup was not so difficult"
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."
"The service is relatively cheap compared to other batch-processing engines."
"The support team is good and it's easy to use."
"I would rate the overall solution a ten out of ten."
"The solution allows us to program in any language we desire."
"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."
 

Cons

"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 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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"Promoting the technology more broadly would help increase its adoption."
"The deployment time could also be reduced."
"Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company."
"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."
"The technical support has slight room for improvement."
"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."
 

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."
"Google Cloud Dataflow is a cheap solution."
"The tool is cheap."
"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 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."
"Google Cloud is slightly cheaper than AWS."
"The solution is cost-effective."
"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%
Healthcare Company
8%
Computer Software Company
8%
Construction Company
8%
Financial Services Firm
20%
Manufacturing Company
12%
Retailer
10%
Computer Software Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise11
 

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 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 feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that man...
What is your primary use case for Google Cloud Dataflow?
The primary use case for Google Cloud Dataflow is when a brand has a lot of data and wants to store it in their warehouse. They can use BigQuery to store their data or use big data solutions to sto...
 

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: April 2026.
886,932 professionals have used our research since 2012.