Apache Spark Certification Practice Test

The assertion that Spark is faster than Hadoop primarily relies on its in-memory processing capabilities. Unlike traditional Hadoop, which predominantly utilizes disk storage and often writes intermediate results to disk, Spark utilizes memory storage to perform operations, significantly reducing the time it takes to process data.

When Spark runs computations, it can store data in RAM across distributed clusters, which allows for much quicker data access and manipulation compared to reading from disk. This in-memory computation enables Spark to perform tasks like iterative algorithms and interactive analytics efficiently, where multiple operations might occur on the same dataset. The reliance on memory instead of constant disk I/O leads to improved performance, especially in workloads requiring multiple passes over the data, such as machine learning and graph processing tasks that can leverage the speed of in-memory data processing.