In the third part of a blog series on building a Twitch streaming graph analysis application, the focus is on streaming data using a Kafka cluster and integrating it with Memgraph, a graph database. The process involves setting up a streaming service using a dummy Python script to connect to Memgraph and run Kafka, with steps for parsing command-line arguments, connecting to Memgraph using the GQLalchemy library, and streaming chatter data from a Twitch streamer called BadBoyHalo. The data is transformed into Cypher queries to update the graph database, creating relationships between chatters and streamers. The implementation is containerized using Docker, with a Dockerfile and a docker-compose.yml file to manage services, which include the backend, frontend, and streaming components. Once running, the application updates the graph with real-time data, affecting metrics like PageRank, and users can customize and extend the application for further insights. The blog encourages feedback and participation in the Memgraph Discord Community Server for discussions and support.