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."Allows us to process batch data, stream to real-time and build pipelines."
"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."
"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."
"The setup was not too difficult."
"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."
"Easy to deploy and manage."
"It is user-friendly and the reporting is good."
"The support team is good and it's easy to use."
"The solution allows us to program in any language we desire."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"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."
"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 could be more user-friendly."
"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."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"There is room for improvement in the initial setup process."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"Apache Flink's documentation should be available in more languages."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"The machine learning library is not very flexible."
"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."
"The authentication part of the product is an area of concern where improvements are required."
"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."
"The solution's setup process could be more accessible."
"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."
"The deployment time could also be reduced."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
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.
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