As both are streaming frameworks which processes event at a time, What are the core architectural differences between these two technologies/streaming framework?
Also, what are some particular use cases where one is more appropriate than the other?
Netflix engineers recently published how they built Studio Search, using Apache Kafka streams, an Apache Flink-based Data Mesh process, and an Elasticsearch sink to manage the index.
Flink offers native streaming, while Spark uses micro batches to emulate streaming. That means Flink processes each event in real-time and provides very low latency. Spark, by using micro-batching, can only deliver near real-time processing. For many use cases, Spark provides acceptable performance levels.
Apache Spark has high latency as compared to Apache Flink. Overall performance of Apache Flink is excellent as compared to any other data processing system. Apache Flink uses native closed loop iterations operators which makes machine learning and graph processing more faster.
Apache Flink is an excellent choice to develop and run many different types of applications due to its extensive features set. Flink's features include support for stream and batch processing, sophisticated state management, event-time processing semantics, and exactly-once consistency guarantees for state.
As you mentioned both are streaming platform which to in memory computation in real time. But there are some architectural differences when you take a closer look.
Note: I am a committer to Apache Apex, so I might sound biased to Apex :)
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