14. Scaling: Dask & Ray

1. The RAM Limit

Pandas crashes if your dataset > RAM. Dask breaks the big dataframe into small chunks and processes them on disk.

2. Distributed Computing

Use Ray to parallelize Python code across a cluster of 100 machines.

python
import ray
ray.init()

@ray.remote
def heavy_task(x):
    return x * x

futures = [heavy_task.remote(i) for i in range(1000)]
results = ray.get(futures)