Allen Wang
Solutions Architect at Confluent
Real-time data has become the backbone of modern software: every fraud check, live recommendation, and AI feature pipeline runs on data in motion. That need has only intensified with AI agents, which depend on fresh, up-to-the-second context to function well. This shift has made engineers who can design and operate streaming systems some of the most sought-after professionals in tech, and demand keeps outpacing supply. I've spent 25 years building these systems at scale, and what's usually missing for newcomers isn't ability, it's someone to make the concepts concrete. That's why I teach: you'll build real, working pipelines and learn the same trade-offs production teams face every day, so you leave ready to step into one of the fastest-growing roles in tech.
About
ALLEN WANG is a real-time data systems architect and engineering leader with 25 years of experience building large-scale systems, currently at Confluent and previously at Netflix, DoorDash and LinkedIn. Wang has architected some of the industry's most demanding real-time data platforms, pioneering the first large-scale deployment of Apache Kafka in the cloud, operating 4,000+ brokers that process over one trillion messages per day, and designing and scaling a real-time event processing platform on Kafka and Flink to handle trillions of events per day at 99.99% reliability. He has contributed to the Apache Kafka open source project (KIP-36) and presented at QCon London, QCon San Francisco, Kafka Summit, and Flink Forward. Alongside his engineering work, Wang has a long record of teaching and mentorship, designing internal engineering bootcamps, leading stream processing training sessions, and hosting conference tracks. He holds an M.S. in Computer Science and Engineering from Michigan State University, and brings this hands-on, production-tested expertise directly into the classroom.
