Ray Tsang shares his experience in writing a custom metrics collector plus an autoscaler using Groovy and Spring Boot, deployed as containerized microservices in Kubernetes.
Leah McGuire describes the machine learning platform Salesforce wrote on top of Spark to modularize data cleaning and feature engineering.
Dustin Hudson discusses enterprise architecture using case studies and life examples to illustrate how to put together legacy systems and third-party apps while considering user-driven decisions.
Josh Evans uses the Netflix Operations Engineering team as a case study to explore the challenges faced by centralized engineering teams and approaches to addressing those challenges.
Erran Berger discusses how they scaled architecture at LinkedIn across multiple data centers.
Haley Tucker and Mohit Vora discuss the architecture at Netflix that makes streaming happen, while highlighting interesting lessons and design patterns that can be widely applied.
The panelists, Alisa Bowen, Pete Steel, Cameron Gough, Lachlan Heasman (moderator), discuss the current status and the future of Agile in the enterprise.
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.
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.