InfoQ Homepage Streaming Content on InfoQ
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Taming Large State: Lessons from Building Stream Processing
Sonali Sharma and Shriya Arora describe how Netflix solved a complex join of two high-volume event streams using Flink.
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Building a Data Exchange with Spring Cloud Data Flow
Channing Jackson presents a case study in the distillation of the finite patterns on each side of the data exchange and a discussion of the patterns used.
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Machine Learning through Streaming at Lyft
Sherin Thomas talks about the challenges of building and scaling a fully managed, self-service platform for stream processing using Flink, best practices, and common pitfalls.
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Real-Time Data Streaming with Azure Stream Analytics
Alexander Slotte introduces Azure Stream Analytics, its ecosystem, and real world examples streaming Twitter feeds as well as sensor data from Raspberry Pi.
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Monitoring and Tracing @Netflix Streaming Data Infrastructure
Allen Wang talks about the design and implementation details of the dev/ops tools used by Netflix and highlights the critical roles they play in operating their data infrastructure.
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Practical Change Data Streaming Use Cases with Apache Kafka & Debezium
Gunnar Morling discusses practical matters, best practices for running Debezium in production on and off Kubernetes, and the many use cases enabled by Kafka Connect's single message transformations.
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Beyond Microservices: Streams, State and Scalability
Gwen Shapira talks about how microservices evolved in the last few years, based on experience gained while working with companies using Apache Kafka to update their application architecture.
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Real-Time Stream Analysis in Functional Reactive Programming
Riccardo Terrell discusses about a reactive approach to application design, and how to account for handling events in near real time employing the Functional Reactive Programming paradigm.
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Announcing Broadway
José Valim discusses how Broadway connects multiple stages and producers, how it leverages GenStage to provide back-pressure, and other features such as batching, rate-limiting, partitioning and more.
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A Dive into Streams @LinkedIn with Brooklin
Celia Kung talks about Brooklin, LinkedIn’s managed data streaming service, and dives deeper into its architecture and use cases, as well as their future plans.
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Streaming Log Analytics with Kafka
Kresten Thorup discusses how and why they use Kafka internally and demos how they utilize it as a straightforward event-sourcing model for distributed deployments.
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Massive Scale Anomaly Detection Framework
Guy Gerson introduces an anomaly detection framework PayPal uses, focusing on flexibility to support different types of statistical and ML models, and inspired by scikit-learn and Spark MLlib.