InfoQ Homepage Event Driven Architecture Content on InfoQ
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Handling Streaming Data in Spotify Using the Cloud
Neville Li and Igor Maravić cover the evolution of Spotify’s event delivery system, discussing lessons learned moving it into the cloud using Scio, a high level Scala API for the Dataflow SDK.
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Large-Scale Stream Processing with Apache Kafka
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
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Staying in Sync: from Transactions to Streams
Martin Kleppmann explores using event streams and Kafka for keeping data in sync across heterogeneous systems, and compares this approach to distributed transactions.
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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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