InfoQ Homepage Streaming Content on InfoQ
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Streaming Auto-scaling in Google Cloud Dataflow
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.
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Microservices for a Streaming World
Ben Stopford discusses using stream processing tools for real-time business apps, handling infinite streams, leveraging high throughput, deploying dynamic, fault-tolerant, and streaming services.
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Real-time Stream Computing & Analytics @Uber
Sudhir Tonse discusses using stream processing at Uber: indexing and querying of geospatial data, aggregation and computing of streaming data, extracting patterns, TimeSeries analyses and predictions.
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Stream Processing with Apache Flink
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs.
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Flying Faster with Heron
Karthik Ramasamy presents the design and implementation of Heron, the new de facto stream data processing engine at Twitter. Ramasamy shares Twitter’s experience of running Heron in production.
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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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High Performance Stream Processing
S Maldini, G Renfro and D Turanski dissect a Spring XD app to show design patterns and techniques for getting the highest throughput and lowest resource utilization in streaming apps.
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Supercharging Operations and Analytics: Using Spring XD to Support Analytics and CEP
Joseph Paulchell discusses the journey from batch-oriented processes using databases to a real-time data streaming solution and the significant benefits achieved as well as the challenges encountered.
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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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IoT Realized - The Connected Car v2
Phil Berman and Michael T Minella present a solution developed with Spring XD to stream real-time analytics from a moving car using open standards.
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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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