When should you use Spark instead of pandas for data processing?

Example Concept: Apache Spark is a distributed data processing framework that excels in handling large datasets across a cluster of machines. It is designed for scalability and speed, leveraging in-memory computation and fault tolerance. In contrast, pandas is a Python library for data manipulation and analysis, best suited for smaller datasets that can be processed on a single machine. Spark's ability to distribute data and computations across multiple nodes makes it more suitable for big data applications, while pandas is ideal for exploratory data analysis and prototyping on smaller datasets.