Can Apache Flink Replace Apache Spark?


Apache Flink and Apache Spark are both distributed and open-sourced processing frameworks built for reducing the latencies of the Hadoop MapReduce in quick data processing. A very common misconception exists in this field and that is Apache Flink will soon be replacing Apache Spark. Is it really possible for both these huge data technologies to co-exist and serve the requirements of fast and fault-tolerant processing.





Flink and Spark might seem quite similar to individuals who have not worked with any of these and are quite familiar with Hadoop. However, it is quite obvious that such individuals will probably feel that the progress of the Apache Flink is superfluous.


Nevertheless, Flink has managed to remain ahead in the competition mainly because of the stream processing feature that it possesses. This feature helps it to manage and process large rows of data in real time. This is something that is not possible with Apache Spark which takes the batch processing methodology into consideration. This is one feature that makes Apache Flink better and faster in comparison to Apache Spark.


As per studies conducted by IBM, there are approximately 2.5 quintillion bytes of data created regularly. It is also to be noted that this rate of generating data is constantly increasing at a very fast pace. Taking things from another perspective, around 90% of data existing throughout the world was processed in the last couple of years in spite of the fact that the World Wide Web is accessible to the public for more than two decades now.


With the growth of the internet, there has been a considerable growth in the number of users and there has been an ever-increasing demand for valid content paving the path for Web 2.0 in the last ten years. This was for the very first time that the users were given the flexibility of creating their very own data on the internet. This data was readily consumable by the audiences who were data hungry.


Both Apache Flink and Apache Spark are next generation Big Data tools grabbing huge industry attention. They serve as the best solutions for different Big Data issues. However, it is to be noted that in comparison to Spark, Flink is faster because of its underlying architecture. Spark has string community support along with several contributors. Nevertheless, as far as streaming capabilities are concerned, Flink serves to be far better that Spark and even possesses native support for the purpose of streaming. Spark is the 3G of Big Data while Flink is the 4G.

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