Why Virtual Reality Needed Stream Processing to Survive

Why Virtual Reality Needed Stream Processing to Survive

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Part 2: Hadoop Made Fast

Why Virtual Reality Needed Stream Processing to Survive

In this talk, we’ll show how a streaming platform can be considered Hadoop Made Fast. With Apache Kafka and it’s Streams API it’s possible to move much of what you would have done in a batch-oriented, sluggish process into a real-time one. We’ll cover the benefits of bringing concepts of Hadoop to real-time applications.

Then, Greg Fodor will share how he's worked with stream processing to solve hard VR challenges. This includes real-time mirroring, capture, and playback of networked avatars in a shared VR environment. Greg will also cover the design patterns they used for Kafka's Streams API and the lessons they learned along the way.

Greg Fodor

Greg Fodor
Co-founder, AltspaceVR

Gehrig Kunz

Technical Product Marketing Manager, Confluent

This is part 2 of 3 in the Streaming in Action: Confluent Online Talk series. Check out the other two talks here.

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