dask

To scale pandas and NumPy workloads beyond RAM, you can use distributed computing with lazy evaluation and chunking. This approach parallelizes computations across multi-core and multi-node setups, enabling out-of-core processing for large datasets.

Yes, you can run parallel pandas DataFrame operations on a single machine. The distributed client orchestrates multi-core execution, allowing you to process data arrays and bags locally without needing a full cluster setup.

Processing unstructured data at scale is handled using parallel data bags. This mechanism distributes unstructured datasets across available compute resources, enabling efficient ETL tasks and large-scale scientific computing.

Lazy evaluation for out-of-core data processing works by building dynamic task graphs and chunking data instead of loading it immediately. Schedulers execute these graphs in parallel, preventing memory overload when handling large datasets.

Yes, this distributed computing approach supports ML pipelines and ETL tasks. By parallelizing NumPy arrays and pandas DataFrames, it handles data transformations and model processing for datasets that exceed memory limits.