dask

To run pandas and NumPy workloads beyond memory limits, use parallel and out-of-core computation to process chunked data across available cores. Lazy evaluation builds task graphs that execute scalably across single machines or distributed clusters without loading everything into memory.

Out-of-core computation processes datasets larger than available RAM by breaking data into chunks and evaluating operations lazily. You need this approach when your tabular data or numerical arrays exceed system memory and require distributed scheduling across workers.

Yes, you can process unstructured data like JSON and text using Bags, which support parallel computation across partitions. Bags handle unstructured data workloads by distributing tasks across multiple workers using flexible thread, process, or distributed scheduling backends.

Parallel DataFrame processing works alongside dynamic task graphs and real-time coordination using Futures. Futures enable dynamic task scheduling and real-time coordination across multi-worker execution, complementing DataFrame and Array processing within the same distributed system.

Out-of-core arrays require chunked data management and rely on lazy evaluation, meaning operations execute only when explicitly triggered. Limitations include scheduling overhead from task graphs and potential latency from disk I/O when processing numerical arrays larger than memory across distributed clusters.