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."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."
"This is truly a real-time solution."
"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"Easy to deploy and manage."
"Apache Flink's best feature is its data streaming tool."
"Allows us to process batch data, stream to real-time and build pipelines."
"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."
"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"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."
"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."
"The service is relatively cheap compared to other batch-processing engines."
"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 support team is good and it's easy to use."
"The machine learning library is not very flexible."
"In a future release, they could improve on making the error descriptions more clear."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"The solution could be more user-friendly."
"There is a learning curve. It takes time to learn."
"Apache Flink's documentation should be available in more languages."
"Google Cloud Dataflow should include a little cost optimization."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"The authentication part of the product is an area of concern where improvements are required."
"They should do a market survey and then make improvements."
"The technical support has slight room for improvement."
"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."
"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."
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 Amazon Kinesis, Spring Cloud Data Flow, 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.
