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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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My Mobile App Only Works on My Phone? How to Scale Enterprise Mobile Apps
The authors discuss patterns and technologies needed to scale large enterprise mobile systems, covering handling network connectivity, data reliability and real-time communication.
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Real-Time Systems at Twitter
Brian Degenhardt discusses lessons that Twitter learned managing a high rate of change and complexity, and how those can be applied anywhere.
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Building Real-time Web Applications with Stratified JavaScript
Alexander Fritze shows how to build real-time web app with Conductance, a web app server built on Stratified JavaScript which includes support for modularity and composability of asynchronous code.
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Design Patterns for Large-Scale Real-Time Learning
Sean Owen provides examples of operational analytics projects, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience Oryx/Cloudera.
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From The Lab To The Factory: Building A Production Machine Learning Infrastructure
Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.
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Samza: Real-time Stream Processing at LinkedIn
Chris Riccomini discusses: Samza's feature set, how Samza integrates with YARN and Kafka, how it's used at LinkedIn, and what's next on the roadmap.
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Add ALL the Things: Abstract Algebra Meets Analytics
Avi Bryant discusses how the laws of group theory provide a useful codification of the practical lessons of building efficient distributed and real-time aggregation systems.
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Stream Processing: Philosophy, Concepts, and Technologies
Dan Frank discusses stream data processing and introduces NSQ – Bitly’s open source queuing system – and other new technologies used for communication between streaming programs.
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High Speed Smart Data Ingest into Hadoop
Oleg Zhurakousky discusses architectural tradeoffs and alternative implementations of real-time high speed data ingest into Hadoop.
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Making the Internet a Better Place: Scaling AppNexus
Mike Nolet shares lessons learned scaling AppNexus and architectural details of their system processing 30TB/day: Hadoop, DNS built in GSLB and Keepalived, and real-time data streaming built in C.
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