Chris Rasmussen discusses NGA's open source strategy, how contributing to open source is changing government partnerships, and the agency's cultural pivot toward a more unclassified future.
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
Aaratee Rao discusses some practical and real world examples of how some well-known hyper growth companies accumulated and managed technical debt.
Nikhil Garg talks about the mental frameworks, processes and tools that allow Quora to strike a good balance and move fast sustainably, both in the short-term and in the long-term.
Roy Rapoport discusses the power of alignment (or lack thereof) using real-world examples, his experience introducing Python in production, and the organizational structures and culture within Netflix
Pete Smith shares from his experience, discussing what it means to fail and how to make the most of it
Ben Hall shares his experience working with Docket for development, testing and deployment into production, discussing scalability, resource management, security and other related issues.
Tony Printezis presents how services are deployed and monitored at Twitter, the benefits of using a custom-built JVM, and the challenges of the use of the JVM in an environment like Twitter.
Sudhir Tonse discusses using stream processing at Uber: indexing and querying of geospatial data, aggregation and computing of streaming data, extracting patterns, TimeSeries analyses and predictions.
Dylan Beattie discusses the challenges faced restructuring Spotlight so their organization aligns with the natural domain boundaries of their business.
Karthik Ramasamy presents the design and implementation of Heron, the new de facto stream data processing engine at Twitter. Ramasamy shares Twitter’s experience of running Heron in production.
Rachel Reese talks about Jet.com's chaos testing methods and code in depth, but also lays out a path to implementation that everyone can use.