Neha Narkhede of Kafka fame shares the experience of building LinkedIn's powerful and efficient data pipeline infrastructure around Apache Kafka and Samza to process billions of events every day.
The authors discuss Netflix's new stream processing system that supports a reactive programming model, allows auto scaling, and is capable of processing millions of messages per second.
Terence Yim from Continuuity showcases a transactional stream processing system that supports full ACID properties without compromising scalability and high throughput.
Gabriel Gonzalez introduces TSAR (TimeSeries AggregatoR), a service for real-time event aggregation designed to deal with tens of billions of events per day at Twitter.
Chris Riccomini discusses: Samza's feature set, how Samza integrates with YARN and Kafka, how it's used at LinkedIn, and what's next on the roadmap.
Richard Tibbetts discusses Complex Event Processing in the context of High Frequency Trading and the advantages of using high level DSLs, followed by the case study of a system built with StreamBase.
Ian Cartwright presents some of his work (developed with Martin Fowler) on event patterns: Event Sourcing, Event Collaboration, Parallel Model, and Retroactive Event.