Another way to understand the difference is through the lineage graph. Narrow transformations have a one-to-one mapping between parent and child RDDs, resulting in a linear lineage graph. Wide transformations have a one-to-many mapping, leading to a complex lineage graph. This distinction is important for optimizing the resilience of RDDs and managing dependencies efficiently.

In addition to the explanation above, narrow transformations are typically used for optimizing performance, as they allow Spark to avoid shuffling and move data across the network. Wide transformations, on the other hand, may introduce some performance overhead due to the need for data shuffling.

Narrow transformations in Spark are operations where each input partition contributes to at most one output partition, such as map or filter. These transformations do not require shuffling and can be executed in parallel. In contrast, wide transformations involve operations where each input partition can contribute to multiple output partitions, requiring data shuffling. Examples of wide transformations include reduceByKey and groupByKey.