Martin Thompson explores how their managed runtimes can equal, and even better in some cases, the performance of native languages.
Jim Webber explores the new Causal clustering architecture for Neo4j, how it allows users to read writes straightforwardly, explaining why this is difficult to achieve in distributed systems.
Dan Luu discusses how to estimate performance using back of the envelope calculations that can be done in minutes or hours, even for applications that take months or years to implement.
Slava Oks talks about SQL Server’s history, high-level architecture and dives into core of I/O Manager, Memory Manager, and Scheduler. Topics include lessons learned and experiences behind the scenes.
Sergii Khomenko introduces best practices in development, covers production deployments to the AWS stack, and using the serverless architecture for data applications.
Frances Perry and Tyler Akidau discuss Apache Beam, out-of-order stream processing, and how Beam’s tools for reasoning simplify complex tasks.
Tom Gianos and Dan Weeks discuss Netflix' overall big data platform architecture, focusing on Storage and Orchestration, and how they use Parquet on AWS S3 as their data warehouse storage layer.
Oleg Zhurakousky provides a quick introduction to Apache NiFi, demonstrates its core features while concentrating on WHY/WHERE and HOW of integrating with Spring.
Uttara Sridhar dives deep into the architecture behind Amazon ECS and demonstrates the key features to build and run a container-based application on Amazon ECS.
Sharma Podila reviews the state of containers usage in Netflix, discussing projects Titus and Mantis, AWS integration, and using Fenzo to run an elastic infrastructure for a varied mix of workloads.
Chase Aucoin explains using Microsoft Service Fabric to create microservices, demoing how to migrate existing services to Service Fabric.
Kevin Hoffmann and Chris Umbel discuss building .NET microservices and deploying them to the Spring Cloud.