Lazy evaluation is a key concept in '. Spark.' that allows for efficient processing of large datasets. It defers the computation of transformations until an action is called, which optimizes performance by minimizing unnecessary computations. When transformations are applied in '. Spark.', they are not fully executed immediately, but rather through a series of dependencies that are built and executed based on actions, such as saving the result to disk or printing it out. This approach avoids unnecessary calculations and improves overall performance.

Sure! Lazy evaluation in '. Spark.' means that transformations on RDDs (Resilient Distributed Datasets) are not immediately executed. Instead, '. Spark.' tracks the operations applied to the dataset and builds a directed acyclic graph (DAG) of the dependencies between these operations. When an action operation is called, '. Spark.' uses this DAG to optimize the execution and compute only the necessary transformations. This approach reduces redundant computations and enables efficient processing of large datasets.

Lazy evaluation is a clever optimization strategy in '. Spark.' that delays the execution of transformations until absolutely necessary. Instead of eagerly computing the transformations, '. Spark.' builds a directed acyclic graph (DAG) that represents the sequence of operations applied to the data. When an action is called, only the necessary transformations are computed, resulting in better performance. This lazy evaluation technique allows '. Spark.' to intelligently optimize the execution and avoid unnecessary work.