We performed a comparison between Apache Flink and Databricks 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."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"Easy to deploy and manage."
"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."
"The documentation is very good."
"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."
"This is truly a real-time solution."
"The solution is very simple and stable."
"The ease of use and its accessibility are valuable."
"The solution is an impressive tool for data migration and integration."
"Databricks integrates well with other solutions."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"Easy to use and requires minimal coding and customizations."
"We have the ability to scale, collaborate and do machine learning."
"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."
"In a future release, they could improve on making the error descriptions more clear."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"The solution could be more user-friendly."
"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."
"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."
"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."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"The integration of data could be a bit better."
"A lot of people are required to manage this solution."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"Can be improved by including drag-and-drop features."
"I believe that this product could be improved by becoming more user-friendly."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Databricks is ranked 2nd in Streaming Analytics with 78 reviews. Apache Flink is rated 7.6, while Databricks is rated 8.2. 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 Databricks writes "A nice interface with good features for turning off clusters to save on computing". Apache Flink is most compared with Spring Cloud Data Flow, Amazon Kinesis, Azure Stream Analytics, Apache Pulsar and Google Cloud Dataflow, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Domino Data Science Platform. See our Apache Flink vs. Databricks report.
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