dask

Out-of-core computation for pandas DataFrames is achieved by building lazy task graphs and executing operations in chunks. This approach partitions larger-than-memory tabular datasets across blocks, enabling parallelized analytics without requiring full local loading.

Blocked arrays extend NumPy's ndarray interface to chunked, larger-than-RAM numerical workloads. This allows array computations to be parallelized into task graphs and executed in manageable chunks, preventing memory overflow errors during processing.

Streaming unstructured text, JSON, and log objects uses Bags to process data iteratively before converting it to structured DataFrames for analysis. This method handles large volumes by building lazy task graphs that execute in chunks.

Multi-file ETL pipelines filter, group, and aggregate data across partitions using parallel operations on DataFrames. The pipeline builds a lazy task graph that executes the entire workflow in chunks when the compute method is called.

Threads, processes, or distributed execution are chosen based on workload characteristics. Threads suit numeric workloads, processes handle GIL-bound operations, and distributed execution provides monitoring and scaling across multiple workers.

Chunk-based task scheduling limitations include overly large task graphs and repeated computations. Performance guardrails involve managing chunk-size, using persist to minimize repeated compute, and monitoring execution via the dashboard.