Apache Spark

Generally, Big Data consists of large-scale and complex data that needs to be processed quickly and efficiently. There are various tools, such as Hadoop, Spark, and Flink, for processing large data. Spark allows in-memory computation, which makes it faster and more efficient compared to Hadoop. Spark’s in-memory computation architecture optimizes performance by retaining intermediate results in RAM whenever possible, effectively eliminating the disk-I/O bottlenecks inherent in traditional frameworks and significantly accelerating execution speeds. Because of these features, Spark can be up to 100 times faster than Hadoop in certain workloads. Spark was developed using Scala.

We can say Spark is another Big Data framework that is based on In-Memory processing. Apache Hadoop mainly processes data by reading from and writing to disk, which can slow down the execution because disk operations consume more time. Spark addresses this limitation by keeping intermediate...