John Bunting talks about different services Tumblr has built and how their architecture helps them be fault tolerant as they continue to grow.
Andy Vaughn gives attendees a case study of how changing the development model and release cycle of a 5 year old software product to continuous delivery greatly improved the product.
Panelists discuss which issues have an impact on the adoption of functional languages, hear how our speakers have addressed these issues and of course we'll have time for a Q&A.
Lisa Van Gelder provides simple tips and tricks for improving delivery without investing lots of time up front creating complex deployment frameworks.
Melody Meckfessel explores how Google's engineering teams use CD to build products and scale them, and how their strain of DevOps speeds launches and helps their engineering culture thrive.
Gil Tene introduces org.ObjectLayout and StructuredArray, the APIs and design considerations that allow Java JDKs to match C on data structure access speeds.
Matei Zaharia talks about the latest developments in Spark and shows examples of how it can combine processing algorithms to build rich data pipelines in just a few lines of code.
Daniel Tunkelang focuses on the data science mindset for successfully applying machine learning to solve problems: express, explain, experiment.
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.