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."
"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."
"It is user-friendly and the reporting is good."
"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."
"Apache Flink's best feature is its data streaming tool."
"Easy to deploy and manage."
"The documentation is very good."
"Allows us to process batch data, stream to real-time and build pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The solution allows us to program in any language we desire."
"It is a scalable solution."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"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."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"There is room for improvement in the initial setup process."
"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."
"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."
"There is a learning curve. It takes time to learn."
"In a future release, they could improve on making the error descriptions more clear."
"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."
"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."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"The solution's setup process could be more accessible."
"The technical support has slight room for improvement."
"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."
"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."
"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."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
"Google Cloud Dataflow should include a little cost optimization."
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.
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