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.
Edmund Jorgensen discusses how and why engineering teams slow down, showing how attempts to manage costs in the face of slowdowns can death-spiral into worse delays with deadly economic consequences.
Claudia Perlich discusses privacy-preserving representations, robust high-dimensional modeling, large-scale automated learning systems, transfer learning, and fraud detection.