Apache Spark Certification Practice Test

When a transformation is applied in Spark, it does not trigger immediate execution of that transformation. Instead, transformations are lazily evaluated, meaning they are recorded in a lineage graph to keep track of operations that need to be performed when an action is eventually called. This allows Spark to optimize the execution plan by constructing a Directed Acyclic Graph (DAG) of the series of transformations leading to the final output, which enhances performance and resource usage.

This concept of lazy evaluation is fundamental in Apache Spark as it enables transformations to be chained together without needing to compute each transformation immediately. The computations are only executed when an action is invoked, such as count, collect, or save. When the action is called, Spark will then process all the transformations that have been defined up to that point in the lineage graph.

The presence of a lineage graph is important as it enables Spark to recompute lost data due to a node failure and provides an efficient way to process complex workflows. Additionally, while data shuffling can occur under certain conditions, it is not a guaranteed outcome of merely applying a transformation; it depends on the specific transformations and the operation being executed.