Martin Kleppmann explores using event streams and Kafka for keeping data in sync across heterogeneous systems, and compares this approach to distributed transactions.
Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.
Ned Twigg discusses using RxJava to wrap SWT events, looking at a few simple SWT UI's, and coding them using raw SWT and then again using RxJava.
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
Sebastien Lambla explores how complexity can be reduced to its smallest cohesive parts, communication normalized through evolvable contracts, ReSTful and event-driven interfaces.
Michael Ploed talks about the distributed data management challenges that arise in a microservices architecture and how they can be solved using event sourcing in an event-driven architecture.
Yongsheng Wu talks about how to build highly-resilient systems at scale. Wu presents also failure cases that prompted engineers at Pinterest to build such systems, and how they test these systems.
Shiva Narayanaswamy discusses event driven architectures, serverless architectures, identity management and security related to building APIs in the cloud.
Steve Pember presents the basic concepts of Event Sourcing, its role on analytics and performance, and the importance of storing historical events to get a view on data at any time.
Neha Narkhede describes Apache Kafka and Samza: scalability and parallelism through data partitioning, fault tolerance, order guarantees, stateful processing, and stream processing primitives.
Marius Bogoevici demoes how to unleash the power of Kafka with Spring XD, by building a highly scalable data pipeline with RxJava and Kafka, using Spring XD as a platform.
Danny Yuan discusses how Uber uses stream processing to solve a wide range of problems, including real-time aggregation and prediction on geospatial time series, and much more.