Matt Ranney covers the evolution of Uber's architecture and some of the systems they built to handle the current scaling challenges.
Marius Bogoevici demoes how to unleash the power of Kafka with Spring XD, by building a highly scalable data pipeline with RxJava and Kafka, using Spring XD as a platform.
S Aerni, S Ramanujam and J Vawdrey present approaches and open source tools for wrangling and modeling massive datasets, scaling Java applications for NLP on MPP through PL/Java and much more.
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.
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.
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.
Jason McCreary takes a look at using background job processes, messaging queues, and cache to help an application scale.
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.
Viktor Gamov covers In-Memory technology, distributed data topologies, making in-memory reliable, scalable and durable, when to use NoSQL, and techniques for Big In-Memory Data.
Ben Stopford examines tools, mechanisms and tradeoffs that allow a data architecture to scale, from disk formats to fully blown architectures for real-time storage, streaming and batch processing.