Flink as a framework overcomes these limitations of Spark streaming also supports exactly once processing which good consistency. It processes data iteratively row by row and is not limited by constraints of micro-batching as in the case of Spark streaming. It also supports time based windowing functions that are very helpful while performing event correlations, while keeping the processing pipeline very flexible and scalable.
The primary feature of Flink which makes it different and very suitable for iterative processing is generally attributed to its near-real-time processing capability. However, it also supports batch processing. Some of the important features of Flink are as follows:
- Exactly once processing makes it a reliable ...
