InfoQ Homepage Event Driven Architecture Content on InfoQ
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Netflix Keystone - How We Built a 700B/day Stream Processing Cloud Platform in a Year
Peter Bakas presents in detail how Netflix has used Kafka, Samza, Docker, and Linux to implement a multi-tenant pipeline processing 700B events/day in the Amazon AWS cloud.
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Connecting Stream Processors to Databases
Gian Merlino discusses stream processors and a common use case - keeping databases up to date-, the challenges they present, with examples from Kafka, Storm, Samza, Druid, and others.
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The Simple Life of ReSTful Microservices
Sebastien Lambla explores how complexity can be reduced to its smallest cohesive parts, communication normalized through evolvable contracts, ReSTful and event-driven interfaces.
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Building Microservices with Event Sourcing and CQRS
Michael Ploed talks about the distributed data management challenges that arise in a microservices architecture and how they can be solved using event sourcing in an event-driven architecture.
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Building Highly-resilient Systems at Pinterest
Yongsheng Wu talks about how to build highly-resilient systems at scale. Wu presents also failure cases that prompted engineers at Pinterest to build such systems, and how they test these systems.
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Light and Fluffy APIs in the Cloud
Shiva Narayanaswamy discusses event driven architectures, serverless architectures, identity management and security related to building APIs in the cloud.
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Richer Data History with Event Sourcing
Steve Pember presents the basic concepts of Event Sourcing, its role on analytics and performance, and the importance of storing historical events to get a view on data at any time.
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Demystifying Stream Processing with Apache Kafka
Neha Narkhede describes Apache Kafka and Samza: scalability and parallelism through data partitioning, fault tolerance, order guarantees, stateful processing, and stream processing primitives.
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Stream Processing at Scale with Spring XD and Kafka
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.
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Stream Processing in Uber
Danny Yuan discusses how Uber uses stream processing to solve a wide range of problems, including real-time aggregation and prediction on geospatial time series, and much more.
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Powering the Industrial Enterprise: Introducing the IOT Platform-as-a-Service
Jesus Rodriguez explores the characteristics of the IOT PaaS vs. predecessor PaaS architectures, focusing on device management, event driven integration, real-time analytics and offline communication.
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Pulsar: Real-time Analytics at Scale
Sharad Murthy & Tony Ng present Pulsar, a real-time streaming system which can scale to millions of events per second with high availability and 4GL language support.
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