Apache Spark Certification 2025 – 400 Free Practice Questions to Pass the Exam

Multi-pass processing is a significant area where Spark demonstrates its advantages over MapReduce. In scenarios that require multiple passes over the data – such as iterating through datasets to perform operations like machine learning algorithms or graph processing – Spark’s in-memory computation model shines.

Spark allows data to be cached in memory across various operations, which drastically reduces the need to read from and write to disk repeatedly. This caching mechanism enables faster access to the dataset for subsequent operations, facilitating efficient execution of processes that involve multiple iterations. In contrast, MapReduce typically necessitates that each pass writes data back to disk, then reads it again for the next operation, which incurs substantial I/O overhead. As a result, while both frameworks can process large datasets, Spark’s optimization around iterative processes makes it far better suited for tasks that require multi-pass computations.