Dive into Apache Airflow 3 & the Airflow AI SDK! Learn to build a League of Legends data analysis pipeline using Google Gemini, Dynamic Task Mapping, & DAG versioning for AI-powered champion insights.

Data Engineer Things

This post demonstrates how to create an end-to-end data pipeline using Airflow 3 and the Airflow AI SDK to analyze League of Legends data. It covers setting up the environment, exploring the Riot Games API, building a Python client for API interaction, and using AI to generate a champion tier list. The pipeline showcases modern Airflow features like Dynamic Task Mapping and emphasizes newer AI integration capabilities with Large Language Models.

Airflow 3 and Airflow AI SDK in Action — Analyzing League of Legends

<p>I’m still learning airflow, so this really intrigues me!</p>
