Erran Berger discusses how they scaled architecture at LinkedIn across multiple data centers.
Stuart Bargon discusses how to “descale” an organization, removing the extra weight and making it agile, showcasing the transformation of one of the oldest Australian public institutions.
Danny Yuan discusses how Uber uses stream processing to solve a wide range of problems, including real-time aggregation and prediction on geospatial time series, and much more.
Nicolas Frankel demoes some of the many important Non-Functional Requirements out-of-the-box that come with Spring Boot: monitoring, metrics, exposing those over HTTP.
Emad Benjamin covers various GC tuning techniques and how to best build platform engineered systems; in particular the focus is on tuning large scale JVM deployments.
Mitchell Hashimoto shows how Terraform and Consul can be used together to easily deploy and scale large-scale containerized workloads using container runtimes like Docker.
Chris Dennis and Alex Snaps discuss introducing caching into a Spring application to solve real world problems.
Jason McCreary takes a look at using background job processes, messaging queues, and cache to help an application scale.
Atlassian Hybrid Cloud/On-Premise Software Delivery and the Journey to 300,000 Applications in the Cloud
George Barnett discusses techniques for building the supporting infrastructure for a hybrid model, and how to make monitoring, deployment tools, and shared services work effectively.
Scott Seighman discusses causes of common performance issues in Big Data environments, heap size, garbage collection, JVM reuse tuning guidelines and Big Data performance analysis tools.
David Fullerton shares some of the things the Stack Exchange tech team have learned along the way while scaling one of the top sites in the world primarily through vertical scaling.
Samy Bahra discusses high performance multicore synchronization, scalability bottlenecks in multicore systems and memory models, and scalable locking and lock-less synchronization.