dask

To process large CSV files exceeding local memory, you can use parallel DataFrame operations to apply pandas-like manipulations on datasets larger than RAM. This approach enables scalable grouping and aggregation by distributing the execution across multiple cores or machines in a cluster.

Yes, you can perform NumPy-style array computations on datasets that do not fit into memory by utilizing distributed array collections. This mechanism splits large numerical arrays into manageable chunks, enabling scientific and numerical tasks to execute efficiently across a cluster.

The best way to process unstructured log files and JSON records at scale is utilizing Bag collections for unstructured data processing. This approach efficiently handles text streams and records by distributing the workload, ensuring parallel execution across available cluster resources.

You do not strictly need a distributed cluster; parallel execution can scale across multiple cores on a single machine or expand to multiple machines. Distributed execution control allows you to manage task dependencies and optimize performance whether deploying locally or across a cluster.

You need parallel computing for Python data analysis workflows when your datasets exceed memory limits or require cluster processing for scalable analysis. It addresses large structured and unstructured data workflows by ensuring efficient task scheduling and execution in distributed environments.