Building with Multiple Languages: A Python-First Approach

Abstract

Python is slow for certain tasks. That’s not news. The usual response is to either accept the performance hit or rewrite everything in another language. There’s a third option: use Python as the control centre and delegate specific tasks to languages that excel at them.

This talk provides practical patterns for building polyglot applications. I’ll show JavaScript handling real-time web interfaces, Rust rendering data visualisations, and C++ accelerating numerical computations—all orchestrated by Python. Through different short live demos and visual explanations, you’ll learn when and how to integrate other languages without sacrificing Python’s strengths, resulting in applications that are both fast and maintainable.

Description

This talk presents three real-world scenarios where Python acts as the glue between specialized languages. We’ll start by walking through the architecture of a data dashboard where JavaScript manages UI interactions while Python handles backend analytics. Using WebSockets and FastAPI, you’ll see how to create responsive interfaces that leverage Python’s data science libraries without the lag. Next, we’ll generate scientific visualizations that render large datasets in real-time, with Rust handling the rendering pipeline while Python manages data processing and provides a familiar API. The demo shows interactive plots processing a significant amount of data points smoothly. Finally, we’ll accelerate a climate model’s core calculations by 100x, where C++ handles the intensive matrix operations while Python manages data I/O, preprocessing, and visualization. Modern tools like pybind11 make these integrations straightforward.

The presentation uses animated diagrams to show data flow between languages and includes live demonstrations of each integration. You’ll learn to identify performance bottlenecks that justify polyglot solutions, understand modern binding tools (PyO3 for Rust, pybind11 for C++, WebSockets for JavaScript), and apply architectural patterns that keep complexity manageable. All examples come from production systems I’ve built or worked on. Attendees will leave with working code examples and a blueprint for building polyglot applications that enhance rather than complicate their Python projects.