Build ML Enhanced Event Streaming Apps with Python Microservices

on YouTube ↗34m

" summary

Apache Pulsar is shown scaling elastically with decoupled storage and compute, 8 data centers full‑mesh geo replication, and a thriving community (600+ contributors, 7k Slack members, 10k commits, 1k+ organizations). The Python client plus Pulsar Functions (Python) run a live sentiment pipeline: web UI → websocket → topic → Python function → output topic, demonstrating built‑in schema registry, Kafka protocol handler, tiered S3 offload, and working end‑to‑end examples with NiFi and Docker.

AI-written summary · about

" chapters (13)

1. 0:00ML enhanced event streaming intro
2. 1:56Apache Pulsar architecture
3. 9:15Pulsar ecosystem support
4. 11:42Pulsar Functions processing
5. 14:06Python client setup
6. 17:43Schema management in Pulsar
7. 20:16MQTT WebSocket for ML
8. 22:00Kafka compatibility
9. 23:23DevOps deployment
10. 24:46Real-time ML app demo
11. 27:11Streaming data in web apps
12. 28:55Data flows with Apache NiFi
13. 30:43Community resources

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