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."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."
"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."
"It is user-friendly and the reporting is good."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"The setup was not too difficult."
"Allows us to process batch data, stream to real-time and build pipelines."
"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."
"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."
"Automation with Databricks is very easy when using the API."
"It's great technology."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"We can scale the product."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."
"The solution is very simple and stable."
"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."
"Apache Flink's documentation should be available in more languages."
"The machine learning library is not very flexible."
"Apache Flink should improve its data capability and data migration."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"In a future release, they could improve on making the error descriptions more clear."
"The solution could be more user-friendly."
"Databricks has a lack of debuggers, and it would be good to see more components."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."
"There are no direct connectors — they are very limited."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"It's not easy to use, and they need a better UI."
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 Amazon Kinesis, Spring Cloud Data Flow, Azure Stream Analytics, Apache Pulsar and Google Cloud Dataflow, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Domino Data Science Platform. See our Apache Flink vs. Databricks report.
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