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.
Sid Anand uses examples from LinkedIn, Netflix, and eBay to discuss some common causes of outages and scaling issues. He also discusses modern practices in availability and scaling in web sites today.
Jay Kreps discusses the evolution of LinkedIn's architecture and lessons learned scaling from a monolithic application to a distributed set of services, from one database to distributed data stores.
John Wang discusses LinkedIn real-time distributed search engine architecture and implementation details for People Search, Signal, Stream Indexing, Zoie, and Bobo.