InfoQ Homepage Event Driven Architecture Content on InfoQ
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Stream Processing & Analytics with Flink @Uber
Danny Yuan discusses how Uber builds its next generation of stream processing system to support real-time analytics as well as complex event processing.
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Demistifying DynamoDB Streams
Akshat Vig and Khawaja Shams discuss DynamoDB Streams and what it takes to build an ordered, highly available, durable, performant, and scalable replicated log stream.
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Cloud Native Streaming and Event-driven Microservices
Marius Bogoevici demonstrates how to create complex data processing pipelines that bridge the big data and enterprise integration together and how to orchestrate them with Spring Cloud Data Flow.
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ETL Is Dead, Long Live Streams
Neha Narkhede shares the experience at LinkedIn moving from ETL to real-time streams, the challenges of scaling Kafka to hundreds of billions of events/day, supporting thousands of engineers, etc.
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Event Sourcery
Sebastian von Conrad and James Ross explain how to use event sourcing in order to keep the cost of change lower.
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Server-Less Design Patterns for the Enterprise with AWS Lambda
Tim Wagner defines server-less computing, examines the key trends and innovative ideas behind the technology, and looks at design patterns for big data, event processing, and mobile using AWS Lambda.
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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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