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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

"Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems."
"The initial setup was not so difficult"
"DataFlow's performance is okay."
"This solution is very scalable and robust."
"The most effective features are data management and analytics."
"This solution is very scalable and robust."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"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."
"Google's support team is good at resolving issues, especially with large data."
"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, and cost-effective if you know how to use it, and 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."
 

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."
"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."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The deployment time could also be reduced."
"Occasionally, dealing with a huge volume of data causes failure due to array size."
"The authentication part of the product is an area of concern where improvements are required."
"The technical support has slight room for improvement."
"Google Cloud Dataflow should include a little cost optimization."
"The solution's setup process could be more accessible."
"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."
 

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."
"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."
"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 Google Cloud Dataflow's pricing a four out of ten."
"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."
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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
13%
Retailer
10%
Insurance Company
5%
 

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.
890,071 professionals have used our research since 2012.