Apache Flink vs Google Cloud Dataflow comparison

"Apache Flink offers a range of powerful configurations and experiences for development teams. Its strength lies in its development experience and capabilities."

"With Flink, it provides out-of-the-box checkpointing and state management, guaranteed message processing, and it also helped us with application maintenance, deployments, and restarts."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

"The main advantage is the turnaround time, which has been reduced drastically because of Apache Flink, and now everything is in almost real time with no waiting or lag of data in the application while machine resources are utilized much more efficiently."

"Apache Flink provides faster and low-cost investment for me; I find it to have low hardware requirements, and it's faster with low code, meaning it's easy to understand for moving the streaming data."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

"Easy to deploy and manage."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."

"The solution allows us to program in any language we desire."

"The integration within Google Cloud Platform is very good."

"The support team is good and it's easy to use."

"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."

"Google Cloud Dataflow is useful for streaming and data pipelines."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"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."

"There is room for improvement in the initial setup process."

"Failure is another area where it is a bit rigid or not that flexible."

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

"One way to improve Flink would be to enhance integration between different ecosystems."

"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."

"Apache Flink should improve its data capability and data migration."

"The authentication part of the product is an area of concern where improvements are required."

"Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company."

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"Occasionally, dealing with a huge volume of data causes failure due to array size."

"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."

"The solution's setup process could be more accessible."

"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."

"Promoting the technology more broadly would help increase its adoption."

"It's an open-source solution."

"The solution is open-source, which is free."

"It's an open source."

"This is an open-source platform that can be used free of charge."

"Apache Flink is open source so we pay no licensing for the use of the software."

"The solution is cost-effective."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"The solution is not very expensive."

"The tool is cheap."

"Google Cloud Dataflow is a cheap solution."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"Google Cloud is slightly cheaper than AWS."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."