InfoQ Homepage Event Stream Processing Content on InfoQ
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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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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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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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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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Reactive Programming at Cloud-Scale and Beyond
Bart de Smet discusses how the reactive programming paradigm can be used for event stream processing and how it can be applied from small devices all the way to cloud-scale infrastructures.
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Reactive Stream Processing at Netflix
Justin Becker & Neeraj Joshi describe Mantis, discuss the challenges associated with designing for the cloud, processing billions of events, all while being cost sensitive.
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Applications of Enterprise Integration Patterns to Near Real-Time Radar Data Processing
Garrett Wampole describes an experimental methodology of applying Enterprise Integration Patterns to the near real-time processing of surveillance radar data, developed by MITRE.
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Samza in LinkedIn: How LinkedIn Processes Billions of Events Everyday in Real-time
Neha Narkhede of Kafka fame shares the experience of building LinkedIn's powerful and efficient data pipeline infrastructure around Apache Kafka and Samza to process billions of events every day.
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Mantis: Netflix's Event Stream Processing System
The authors discuss Netflix's new stream processing system that supports a reactive programming model, allows auto scaling, and is capable of processing millions of messages per second.
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High Throughput Stream Processing with ACID Guarantees
Terence Yim from Continuuity showcases a transactional stream processing system that supports full ACID properties without compromising scalability and high throughput.
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