What is the main benefit of using lazy evaluation in Spark?

The main benefit of using lazy evaluation in Spark is that it reduces computational resource usage by delaying execution. In lazy evaluation, Spark builds a directed acyclic graph (DAG) of the transformations applied to the data rather than executing them immediately. This allows Spark to optimize the execution plan before processing the data, which can lead to fewer computations and a more efficient use of resources.

By deferring the execution until an action (such as a count or collect) is called, Spark can analyze the entire computation pipeline and make optimizations, like reordering operations and minimizing shuffles. This results in potentially significant savings in terms of both time and computational costs, especially for large datasets and complex transformations.

The other options refer to features or scenarios that are not directly related to the main advantage of lazy evaluation. Immediate optimization of all data transformations is not how Spark operates, as optimizations occur only at the execution stage. Instant feedback on query performance isn't a function of lazy evaluation; instead, it may come from query execution analysis tools. Lastly, while live data updates can occur in streaming scenarios, lazy evaluation itself is focused on optimizing batch processing rather than real-time updates during execution.