Which technique does broadcasting in Spark utilize?

Broadcasting in Spark is a technique that involves sending a read-only copy of a dataset to all worker nodes in the cluster. This approach is particularly useful when you have a small dataset that needs to be used across multiple stages of a computation, as it minimizes the need for data shuffling across the network. By distributing this smaller dataset to each worker, Spark can efficiently perform operations without incurring the overhead of repeatedly pulling data from a centralized location.

Through broadcasting, operations can take place more quickly since the worker nodes already possess the necessary data. This is especially significant in situations where the dataset is small relative to the amount of data being processed in parallel. Essentially, it optimizes the computation by making relevant data readily available without the need for repeated access to a central repository, which can be a performance bottleneck.

In contrast, other options describe different behaviors that are not aligned with the concept of broadcasting. Sending large datasets to the driver node can overwhelm it and lead to inefficiencies. Executing tasks in parallel at the cluster level pertains to the overall distributed computing paradigm, not specifically to broadcasting. Reducing the number of worker nodes is a separate consideration and not directly related to the idea of broadcasting data across nodes. Thus, the essence of broadcasting focuses on