twitch-analytics-demo VS twitch-analytics-demo

When we’re building demo applications to showcase Memgraph, we always use Docker Compose. This allows us to fire up the application on any system, which is useful when showing a demo at conferences or meetups. Also, applications created with Docker Compose are much easier to deploy. One such demo is the Twitch analytics demo. In the docker-compose.yml file, we defined a couple of services:

While building the whole web application, you save the necessary data to the database and load it whenever needed. Fetching the data can be error-prone since there may be that one minor typo that will bug you. In this tutorial, you'll learn how to build a small part of the Twitch analytics app with the help of GQLAlchemy, an object graph mapper (OGM) that makes building graph-based apps much easier.

To get started, read Part 1 and Part 2. If you want to skip that and hop right on the streaming part, you can find the backend and frontend implementations from the first two parts here.

If you still haven't, you can read already published Part 1 and then continue reading this part. Otherwise, use already implemented backend. In this part, we are going to create React application and visualize general statistics and some interesting insights from Twitch dataset. All implementation that will be mentioned in this part of the blog you can find in frontend folder of the project.

Sure, here is the scraper: https://github.com/memgraph/twitch-analytics-demo/tree/main/... and let me know if you have any questions regarding the Twitch API, since you have to make your own account :)

The data was collected using Twitch API. The data needed to be rearranged so that it could fit the idea of graph databases. Here you can find the script that creates .csv files which we'll load into Memgraph. The files which we'll use are: streamers.csv, teams.csv, vips.csv, moderators.csv and chatters.csv. In streamers.csv we can find important information about languages the user speaks and games the user streams. Those two will actually be nodes in our graph database.

Here you can see we are copying package.json into the container. The best way to have all dependencies installed is to copy package.json file from here. All node modules will then be correctly installed in the container and npm start will run your React app. Node modules volume was added so that all packages don't have to be installed each time you build your project.