Dask - Scalable Parallel Computing

Dask provides high-level collections (Arrays, DataFrames, Bags) that mimic the APIs of NumPy and pandas but operate in parallel on data sets that are larger than memory.

When to Use

• Processing datasets that don't fit in RAM (Out-of-core computing).
• Speeding up computations by using all available CPU cores.
• Parallelizing custom Python functions or complex workflows (dask.delayed).
• Scaling machine learning pipelines to large clusters.
• Handling large-scale arrays in physics, climate science, or imaging.
• Analyzing massive log files or unstructured data (dask.bag).

Reference Documentation

Official docs: https://docs.dask.org/
Dask Examples: https://examples.dask.org/
Search patterns: dask.dataframe, dask.array, dask.delayed, client.compute, dask.distributed