dask

Out-of-core computation handles large pandas DataFrames by chunking data and processing it in parallel across available cores or clusters, enabling scalable tabular data processing without exceeding system memory.

Parallel NumPy array computations on large datasets are best handled by chunked arrays that distribute numeric calculations across multiple workers, allowing out-of-core processing for big array workloads beyond single-machine memory limits.

Yes, parallel pandas operations support large-scale ETL workflows by applying lazy evaluation and task graphs to distribute data transformations, filtering, and aggregations efficiently across single machines or distributed cluster environments.

Out-of-core computing works for unstructured data like text and JSON by using functional pipelines that process data in parallel chunks, enabling efficient handling of large unstructured datasets that exceed memory capacity.

Use distributed schedulers instead of local parallel execution when your dataset size or computation complexity requires scaling across multiple machines in a cluster, whereas local execution suffices for single-machine out-of-core workloads within RAM limits.

Lazy evaluation requires building task graphs upfront, which limits dynamic workflow flexibility, but you can use Futures for dynamic task submission to enable real-time execution control across distributed schedulers.