Lazy evaluation in '. Spark.' refers to the approach of delaying the execution of computations until their results are absolutely required. This means that operations and transformations on RDDs are only performed when an action is called upon the dataset. By using lazy evaluation, '. Spark.' can optimize the execution plan and perform operations more efficiently. For example, it can skip unnecessary computations or consolidate multiple steps into a single operation, which can significantly improve the performance of a program.

Lazy evaluation is a fundamental concept in '. Spark.' that helps optimize the execution of computations. Instead of eagerly executing transformations on RDDs, '. Spark.' postpones the evaluation until the results are actually required. This allows for more efficient execution plans, as unnecessary computations can be skipped. By delaying the evaluation, '. Spark.' can also optimize data shuffling and minimize disk I/O, leading to improved performance. So, if you're dealing with large datasets, utilizing lazy evaluation in '. Spark.' can make a big difference in terms of performance.

Lazy evaluation is a powerful feature in '. Spark.' that allows for efficient processing of large datasets. Instead of immediately executing operations on an RDD, '. Spark.' evaluates them lazily, meaning it postpones the execution until the result is actually needed. This can save computational resources by eliminating unnecessary calculations. By chaining multiple transformations together and executing them only when an action is triggered, '. Spark.' can optimize the execution plan and increase overall performance.