Can we run automatic operations in Python? Why don't we do that? Why use Path? Even distributed tasks can be done using Python, so why use Path at all?

Python is a versatile language and can certainly run automated scripts and small‑to‑medium batch jobs. However, when the data size grows into the terabyte or petabyte range, a single Python process cannot efficiently distribute work across a cluster, manage memory, or fault‑tolerant execution. Spark provides a distributed execution engine that automatically partitions data, schedules tasks on multiple workers, and handles failures. The term "Path" in this context usually refers to the file system path (e.g., HDFS, S3) where data resides. Spark reads data from these paths in a parallel fashion, whereas a plain Python script would read the file sequentially, potentially exhausting local memory. Therefore, while Python can perform the logic, Spark is needed to scale the computation, provide resilience, and integrate with big‑data storage systems. Using Path is essential because it tells Spark where to locate the data for distributed processing; without it, Spark cannot parallelize the workload.