We performed a comparison between Apache Flink 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."This is truly a real-time solution."
"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"It is user-friendly and the reporting is good."
"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"The documentation is very good."
"Apache Flink's best feature is its data streaming tool."
"The service is relatively cheap compared to other batch-processing engines."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"The support team is good and it's easy to use."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"It is a scalable solution."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"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."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"Apache Flink should improve its data capability and data migration."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"There is a learning curve. It takes time to learn."
"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."
"There is room for improvement in the initial setup process."
"The machine learning library is not very flexible."
"The technical support has slight room for improvement."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"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."
"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 authentication part of the product is an area of concern where improvements are required."
"The deployment time could also be reduced."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Apache Flink is rated 7.6, while Google Cloud Dataflow is rated 7.8. The top reviewer of Apache Flink writes "A great solution with an intricate system and allows for batch data processing". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Apache Flink is most compared with Spring Cloud Data Flow, Amazon Kinesis, Databricks, Azure Stream Analytics and Informatica Data Engineering Streaming, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and Informatica Data Engineering Streaming. See our Apache Flink vs. Google Cloud Dataflow report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
