Modern web applications are no longer just "sites." They are long-lived, highly interactive systems that span multiple runtimes, global content delivery networks, edge caches, background workers, and increasingly complex data pipelines. They are expected to load instantly, remain responsive under poor network conditions, and degrade gracefully when something goes wrong.
Uber's engineering team has transformed its data replication platform to move petabytes of data daily across hybrid cloud and on-premise data lakes, addressing scaling challenges caused by rapidly growing workloads. Built on Hadoop's open-source Distcp framework, the platform now handles over one petabyte of daily replication and hundreds of thousands of jobs with improved speed, reliability, and observability.
Halpern co-authored three books and contributed to more than 300 research papers over the course of his 45-year career. "He was a towering figure in theoretical computer science and a beloved faculty member," said Lorenzo Alvisi, the Tisch University Professor in Computer Science and chair of the Department of Computer Science in the Cornell Ann S. Bowers College of Computing and Information Science. "Joe was as close to the ideal of a Renaissance man as I have met."
Steve Yegge thinks he has the answer. The veteran engineer - 40+ years at Amazon, Google and Sourcegraph - spent the second half of 2025 building Gas Town, an open-source orchestration system that coordinates 20 to 30 Claude Code instances working in parallel on the same codebase. He describes it as "Kubernetes for AI coding agents." The comparison isn't just marketing. It's architecturally accurate.
Support for distributed systems. Check how well the tool handles microservices, serverless, and Kubernetes. Can you follow a request across services, queues, and third-party APIs? Does it understand pods, nodes, clusters, and autoscaling events, or does it treat everything like a static host? Correlation across metrics, logs, and traces. In an incident, you shouldn't be copying IDs between tools. Look for the ability to pivot directly from a slow trace to relevant logs,
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Data deletion in distributed systems presents challenges that extend far beyond simple database operations. Engineers face a fundamental dilemma: the fear of accidentally destroying critical information keeps teams cautious, yet failing to delete data can expose them to legal risks under regulations like GDPR, increase storage costs, and erode customer trust. "Deletion can't be an afterthought," the presenters emphasized. A major driver of Netflix's platform is managing testing data generated by frequent end-to-end production tests.
Airbnb has developed Impulse, an internal load testing framework designed to improve the reliability and performance of its microservices. The tool enables distributed, large-scale testing and allows engineering teams to run self-service, context-aware load tests integrated with CI pipelines. By simulating production-like traffic and interactions, Impulse helps engineers identify bottlenecks and errors before changes reach production. According to the Airbnb engineering team, Impulse is already in use in several customer support backend services and is under review for broader adoption.
I'm going to be talking about something very basic, which is almost like the fundamental ideas that I think that every developer should know if they're unfortunate enough to have to work on a distributed system, which is probably most of you. The reason I've written this talk is because I'm working on a book, which is designed as like, you've been dropped onto a project where somebody ill-advisedly made the choice to use microservices, which is always a terrible idea.
At the heart of .NET Aspire is its dashboard, which gives you many of the necessary observability and management tools for your code. You can launch and debug applications, and at the same time watch their performance and behavior along with external resources running in containers.
Agentic AI has become a priority with enterprises everywhere as a new model that can potentially replace enterprise software as we understand it today. With today's announcement, we're making it easy for customers to build their distributed systems, including agentic AI systems, without having to commit to the Akka Platform.
