Shakhina Pulatova overviews the Instant Search experience at LinkedIn and how they use Machine Learning to deliver personalized results as the query is typed.
Hans Dockter discusses how to solve the challenges of standardization, dependency management, multi-language builds, and automatic build infrastructure provisioning.
Erran Berger discusses how they scaled architecture at LinkedIn across multiple data centers.
Omer Shapira introduces HTTP/2 (and SPDY), exploring the impact the protocol has on application design, and telling the story of LinkedIn adopting SPDY on its network infrastructure.
Steven Ihde and Karan Parikh discuss about tools and frameworks built in order to help LinkedIn's transition to microservices, including their URN resolution engine and the Rest.li API Hub.
Lin Qiao discusses the architecture of Gobblin, LinkedIn’s framework for addressing the need of high quality and high velocity data ingestion.
Jason Toy talks about the evolution and history of LinkedIn's release strategy.
Sid Anand discusses the architectural and development practices adopted by LinkedIn as a continuous growing company.
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 some of the unique challenges they've faced delivering highly personalized search over semi-structured data at massive scale.
Kiran Prasad discusses what impact mobile has on architecture, explaining how HTML5 and Node.js can help, and sharing how to use these technologies effectively at scale.