Learn Moose: Building a Real-time GitHub Analytics Service
Welcome to the Moose framework tutorial! In this hands-on guide, you'll build a complete real-time analytics service that tracks and analyzes users who star your GitHub repository.
What You'll Learn
By the end of this tutorial, you'll understand:
- The core Moose primitives and how they work together
- How to build a complete data pipeline from ingestion to insights
- Best practices for real-time data processing with Moose
Prerequisites
- Basic knowledge of TypeScript or Python
- A GitHub account
- Installation of Moose CLI (we'll cover this in the first step)
Time to Complete
This tutorial takes approximately 30-60 minutes to complete.
What You'll Build
You'll create a complete end-to-end analytics service that:
- Captures data - Ingest GitHub webhook events in real-time when users star your repository
- Processes events - Filter and enrich the raw data with additional user information
- Aggregates data - Create materialized views that aggregate information about your repository's popularity in real-time
- Exposes insights - Build APIs that expose the aggregated data to client applications
Let's Get Started!
In the next section, we'll set up your Moose project and create your first Data Model to start ingesting sample data.