We performed a comparison between Apache Flink and Apache Spark Streaming 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."Easy to deploy and manage."
"The setup was not too difficult."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"Apache Flink's best feature is its data streaming tool."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."
"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."
"Allows us to process batch data, stream to real-time and build pipelines."
"This is truly a real-time solution."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is better than average and some of the valuable features include efficiency and stability."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"The solution is very stable and reliable."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."
"In a future release, they could improve on making the error descriptions more clear."
"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."
"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."
"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."
"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."
"Apache Flink's documentation should be available in more languages."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The solution itself could be easier to use."
"It was resource-intensive, even for small-scale applications."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews. Apache Flink is rated 7.6, while Apache Spark Streaming is rated 8.0. 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 Apache Spark Streaming writes "Easy integration, beneficial auto-scaling, and good open-sourced support community". Apache Flink is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Azure Stream Analytics and WSO2 Stream Processor, whereas Apache Spark Streaming is most compared with Amazon Kinesis, Azure Stream Analytics, Spring Cloud Data Flow, Confluent and SAS Event Stream Processing. See our Apache Flink vs. Apache Spark Streaming report.
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