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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
14th
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
7th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Cloudera DataFlow is 0.9%, down from 1.5% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Mohamed-Saied - PeerSpot reviewer
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
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."
"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."
"This solution is very scalable and robust."
"The integration within Google Cloud Platform is very good."
"The service is relatively cheap compared to other batch-processing engines."
"It is a scalable solution."
"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."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."
"The solution allows us to program in any language we desire."
"I would rate the overall solution a ten out of ten."
 

Cons

"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."
"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."
"The technical support has slight room for improvement."
"Promoting the technology more broadly would help increase its adoption."
"The solution's setup process could be more accessible."
"They should do a market survey and then make improvements."
"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."
"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."
"The deployment time could also be reduced."
"The authentication part of the product is an area of concern where improvements are required."
 

Pricing and Cost Advice

"DataFlow isn't expensive, but its value for money isn't great."
"The solution is not very expensive."
"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."
"Google Cloud Dataflow is a cheap solution."
"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 Google Cloud Dataflow's pricing a four 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."
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Top Industries

By visitors reading reviews
University
18%
Computer Software Company
16%
Financial Services Firm
14%
Media Company
6%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

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?
I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...
 

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: March 2025.
847,772 professionals have used our research since 2012.