Apache Spark Certification Practice Test 2025 – All-in-One Guide to Excel in Your Certification Exam

The speed advantage of Spark over Hadoop, particularly when operating on disk, is noteworthy due to several foundational differences between the two systems. Spark's processing model is designed to optimize memory usage, which leads to significantly faster performance when compared to traditional disk-based computations often used in Hadoop.

Spark utilizes in-memory processing, which means it keeps data in RAM instead of reading from and writing to disk for every operation. This is key because accessing data in memory is orders of magnitude faster than accessing data on disk, where Hadoop's MapReduce framework traditionally operates with a more disk-centric approach. Each MapReduce job requires writing intermediate data to disk, which adds latency and reduces the overall speed of processing. In contrast, Spark minimizes disk I/O through its resilient distributed datasets (RDDs) and can cache data in memory across cluster nodes for faster access.

The stated speed advantage of approximately 10 times reflects the empirical performance differences seen in various workloads and benchmarks, showing how Spark can execute tasks more rapidly when utilizing standard disk-based processing compared to Hadoop. This performance edge is particularly evident in iterative algorithms, such as those commonly used for machine learning and graph processing, where multiple passes through the data are necessary. The effectiveness of Spark in such contexts further solidifies its reputation as a