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."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."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"The documentation is very 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."
"Easy to deploy and manage."
"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."
"This is truly a real-time solution."
"As an open-source solution, using it is basically free."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is very stable and reliable."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"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 was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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."
"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."
"There is room for improvement in the initial setup process."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"There is a learning curve. It takes time to learn."
"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 machine learning library is not very flexible."
"In a future release, they could improve on making the error descriptions more clear."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"In terms of improvement, the UI could be 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."
"It was resource-intensive, even for small-scale applications."
"The solution itself could be easier to use."
"The initial setup is quite complex."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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."
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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We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
