InfoQ Homepage Enterprise Architecture Content on InfoQ
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Airbnb’s Great Migration: Building Services at Scale
Jessica Tai recaps her QCon SF 2018 “Great Migration” presentation then continues the story with a focus on how Airbnb is building, operating, and scaling its expanding network of services.
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Global Event Streams Made Simple with Spring Cloud Stream & Cloud Pub/Sub
Artem Bilan, Kir Titievsky discuss the Google Cloud Pub/Sub binder for Spring Cloud Stream.
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Point-to-Point Messaging Architecture - The Reactive Endgame
Ryland Degnan, Stephane Maldini explore the current state of messaging architecture and provide an R&D perspective on the future of distributed systems.
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Intel's Cloud-Native Transformation
Liel Chayoun and Roi Ezra discuss Intel’s transition to cloud-native and microservices.
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Reasoning about Uncertainty at Scale
Max Livingston presents a case study of using Bayesian modelling and inference to directly model behavior of aircraft arrivals and departures, focusing on the uncertainty in those predictions.
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Solving Business Problems with AI
The panelists discuss using AI to solve business problems.
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Modern Messaging with RabbitMQ, Spring Cloud and Reactor
Arnaud Cogoluègnes demos messaging apps built with RabbitMQ with Reactor on Spring Cloud. Code used in this talk is made available for download.
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The Great Migration: from Monolith to Service-Oriented
Jessica Tai provides an overview of trade-offs and motivation for the SOA migration and discusses Airbnb’s architectural tenets around service building.
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Testing the Endpoints of Your REST APIs
Tonya Cooper discusses how to test REST API endpoints through unit testing the controllers and integration testing with Postman.
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Goodbye REST APIs. Hello GraphQL!
Cory House discusses GraphQL, what makes it attractive, how GraphQL integrates with React, Angular, Vue and others, and how to migrate from REST APIs.
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Your Project Behaves Like a Hurricane. Forecast It Like One
Daniel Vacanti discusses using techniques like Monte Carlo Simulation and Continuous Forecasting to make projects more predictable.
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Engineering Systems for Real-Time Predictions @DoorDash
Raghav Ramesh presents DoorDash’s thoughts on how to structure ML systems in production to enable robust and wide-scale deployment of ML, and shares best practices in designing engineering tooling.
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