Elliot Chow discusses the data pipeline that they built with Kafka, Spark Streaming, and Cassandra to process Netflix user activities in real time for the Trending Now row.
Ali Basiri discusses the motivation behind ChAP (Chaos Automation Platform), how they implemented it, and how Netflix service teams are using it to identify systemic weaknesses.
Kolton Andrus and Peter Alvaro present how a “big idea” -- lineage-driven fault injection -- evolved from a theoretical model into an automated failure testing service at Netflix.
Sayli Karmarkar discusses Winston, a monitoring and remediation platform built for Netflix engineers.
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
Christian Posta explains building microservices with Spring, Spring Cloud, and Netflix OSS and running them on Docker and Kubernetes.
Sharma Podila reviews the state of containers usage in Netflix, discussing projects Titus and Mantis, AWS integration, and using Fenzo to run an elastic infrastructure for a varied mix of workloads.
Tomas Lin discusses Spinnaker and SpEL, an open source, multi-cloud continuous delivery platform that is used by over 90% of cloud deployments at Netflix.
Travis Cherry and Mary Ann Wayer discuss monolithic architectural patterns, JBoss apps, lessons learned moving to Spring MVC SPA, then microservices with Spring Boot, Netflix OSS and Spring Cloud.
Casey Rosenthal talks about a new discipline called Intuition Engineering and Vizceral, a tool they built at Neflix to process massive amounts of visual data in parallel.
Haley Tucker discusses how other systems may affect Netflix' services, strategies to protect their systems and make sure they won't fail even if things go wrong.
Josh Evans talks about the chaotic and vibrant world of microservices at Netflix, exploring the cultural, architectural, and operational methods that lead to microservice mastery.