Retrospect is a system for scalable data analysis that combines the simplicity of Python with built‑in map‑reduce, a fast abstract machine, and a framework for distributed computation, aiming to provide easy‑to‑use, expressive, and scalable data analysis.
This article is part 4 of a crash course on the Model Context Protocol (MCP). It focuses on resources and prompts, explaining their mechanics, distinctions, and implementation, and how they differ from tools. It covers resource types, discovery mechanisms, and application-controlled access patterns.
Learn how to create and use Polars LazyFrames for efficient data processing. Discover lazy evaluation, predicate and projection pushdown, and how to handle large datasets.
