We performed a comparison between Cloudera DataFlow and Google Cloud Dataflow based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The initial setup was not so difficult"
"This solution is very scalable and robust."
"DataFlow's performance is okay."
"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."
"It is a scalable solution."
"The solution allows us to program in any language we desire."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"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."
"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"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
"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."
"Google Cloud Dataflow should include a little cost optimization."
"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."
"They should do a market survey and then make improvements."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The technical support has slight room for improvement."
"The authentication part of the product is an area of concern where improvements are required."
Cloudera DataFlow is ranked 13th in Streaming Analytics with 3 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Cloudera DataFlow is rated 6.6, while Google Cloud Dataflow is rated 7.8. The top reviewer of Cloudera DataFlow writes "A scalable and robust platform for analyzing data". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Cloudera DataFlow is most compared with Databricks, Confluent, Amazon MSK, Spring Cloud Data Flow and Talend Data Streams, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and Spring Cloud Data Flow. See our Cloudera DataFlow vs. Google Cloud Dataflow report.
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