InfoQ Homepage Apache Kafka 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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Rethinking Streaming Analytics for Scale
Helena Edelson addresses new architectures emerging for large scale streaming analytics based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK) or Apache Flink or GearPump.
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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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Developing Real-time Data Pipelines with Apache Kafka
Joe Stein makes an introduction for developers about why and how to use Apache Kafka. Apache Kafka is a publish-subscribe messaging system rethought of as a distributed commit log.
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Spring XD Today and Tomorrow
Mark Pollack discusses Spring XD and its integration driven by the Big Data ecosystem at large such as Kafka, Spark, functional programming, integration with Python, and designer/monitoring UIs.
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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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The Many Faces of Apache Kafka: How is Kafka Used in Practice
Neha Narkhede discusses how companies are using Apache Kafka and where it fits in the Big Data ecosystem.
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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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The Game of Big Data: Scalable, Reliable Analytics Infrastructure at KIXEYE
Randy Shoup describes KIXEYE's analytics infrastructure from Kafka queues through Hadoop 2 to Hive and Redshift, built for flexibility, experimentation, iteration, testability, and reliability.