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.
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.
Greg Young discusses unexpected use cases and possible usages of the Event Store.
Ward Cunningham keynotes on how Events, Sockets, CORS, Closures, SVG, DSLs, Canvas, EC2 and Raspberry Pi contribute to a new type of wiki, a federated one.
Paul Buhler provides insight into the development and application of a semantically grounded version of the Workflow Management Coalition's Business Process Analytics Format (BPAF) specification.
Uri Cohen discusses several types of queues with their pros and cons used in financial and trading industries for highly parallelized data processing.
Yves Reynhout discusses event sourcing and storage, demoing implementing a conceptual event storage model on top of AWS Storage and Azure Storage Services.
Martin Thompson discusses achieving high availability by using an event sourced architecture in which changes of the system’s state is captured as a sequence of events.
Donald Belcham presents the Event Aggregator pattern and the event problems it solves: tight coupling, refactoring difficulty, object chaining, memory leak, showing how to build one.
Greg Young discusses how to use events to store data, and how testing, versioning and performance are impacted by an event-centered model.
Our application runs over 10,000 sustained transactions per second with a rich model. The key? Modeling state transitions explicitly.