PySpark vs Pandas: when should you move to Spark?

Pandas is designed for in-memory data and works best with small-to-moderate datasets that fit on a single machine. If the data becomes too large for memory, processing slows down or crashes. Moving to PySpark is needed when datasets become very large, exceed a single machine’s memory, or require distributed computing across clusters.