We performed a comparison between Apache Flink and Cloudera 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."The documentation is very good."
"Allows us to process batch data, stream to real-time and build pipelines."
"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."
"This is truly a real-time solution."
"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."
"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."
"DataFlow's performance is okay."
"This solution is very scalable and robust."
"The initial setup was not so difficult"
"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."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"The machine learning library is not very flexible."
"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."
"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."
"Although their workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages."
"It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning."
"It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Cloudera DataFlow is ranked 13th in Streaming Analytics with 3 reviews. Apache Flink is rated 7.6, while Cloudera DataFlow is rated 6.6. 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 Cloudera DataFlow writes "A scalable and robust platform for analyzing data". Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Azure Stream Analytics and Apache Pulsar, whereas Cloudera DataFlow is most compared with Databricks, Confluent, Amazon MSK, Informatica Data Engineering Streaming and Hortonworks Data Platform. See our Apache Flink vs. Cloudera DataFlow report.
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