InfoQ Homepage Event Stream Processing Content on InfoQ
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From Confusion to Clarity: Advanced Observability Strategies for Media Workflows at Netflix
Naveen Mareddy and Sujana Sooreddy explain how Netflix monitors massive media encoding workflows. They discuss scaling to 1M+ trace spans and using Flink to unlock real-time business insights.
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Building a Large Scale Real-Time Ad Events Processing System
Chao Chu provides insights and practical knowledge for building streaming pipelines for an ad platform.
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From Zero to a Hundred Billion: Building Scalable Real-Time Event Processing at DoorDash
Allen Wang discusses the design of the event system including major components of event producing, event processing with Flink and streaming SQL, event format and schema validation.
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Reactive Event Processing with Apache Geode
Bill Burcham discusses how to integrate Geode with your Reactive System efficiently, and at scale.
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Building Cloud-Native Data-Intensive Applications with Spring
Sabby Anandan and Soby Chako discuss how Spring Cloud Stream and Kafka Streams can support Event Sourcing and CQRS patterns.
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Drivetribe: A Social Network on Streams
Aris Koliopoulos talks about how common problems in social media can be resolved with a healthy mix of stream processing and functional programming.
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Stream Processing & Analytics with Flink @Uber
Danny Yuan discusses how Uber builds its next generation of stream processing system to support real-time analytics as well as complex event processing.
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Demistifying DynamoDB Streams
Akshat Vig and Khawaja Shams discuss DynamoDB Streams and what it takes to build an ordered, highly available, durable, performant, and scalable replicated log stream.
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ETL Is Dead, Long Live Streams
Neha Narkhede shares the experience at LinkedIn moving from ETL to real-time streams, the challenges of scaling Kafka to hundreds of billions of events/day, supporting thousands of engineers, etc.
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Server-Less Design Patterns for the Enterprise with AWS Lambda
Tim Wagner defines server-less computing, examines the key trends and innovative ideas behind the technology, and looks at design patterns for big data, event processing, and mobile using AWS Lambda.
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Large-Scale Stream Processing with Apache Kafka
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
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Staying in Sync: from Transactions to Streams
Martin Kleppmann explores using event streams and Kafka for keeping data in sync across heterogeneous systems, and compares this approach to distributed transactions.
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