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."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."
"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."
"Allows us to process batch data, stream to real-time and build pipelines."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"The documentation is very good."
"It is user-friendly and the reporting is good."
"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."
"The setup was not too difficult."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"Databricks is a scalable solution. It is the largest advantage of the solution."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"I work in the data science field and I found Databricks to be very useful."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"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."
"Apache Flink's documentation should be available in more languages."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"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."
"The solution could be more user-friendly."
"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."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"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."
"Overall it's a good product, however, it doesn't do well against any individual best-of-breed products."
"Databricks has a lack of debuggers, and it would be good to see more components."
"Anyone who doesn't know SQL may find the product difficult to work with."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"A lot of people are required to manage this solution."
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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