How Netflix Uses Apache Kafka

Netflix engineered a real-time recommendation delivery system for live events that can update over 100 million devices in under a minute.

Netflix built a Real-Time Distributed Graph (RDG) to connect member interaction data across their expanding business verticals including streaming, live events, and mobile games.

The article discusses the implementation of a system at Netflix for tracking 'impressions'—the visual elements users interact with while browsing content.

This article discusses Netflix's Distributed Counter Abstraction, a service designed to enable distributed counting at scale while maintaining low latency performance.

The article discusses the development of a data reprocessing pipeline within Netflix's Asset Management Platform (AMP), designed to efficiently manage and update digital media assets' metadata.

The article discusses Netflix's auto-diagnosis and remediation system, Pensive, which addresses failures in their complex data platform.

The article discusses the lessons learned from building observability tools at Netflix, emphasizing the importance of log management, distributed request tracing, metrics analysis, and user interfa...

The article discusses Netflix's ongoing journey in platform engineering, highlighting the evolution of their cloud ecosystem, engineering challenges, and the adoption of new technologies.

The article discusses Simone, a distributed simulation service developed by Netflix that automates the testing and certification of its app on various devices.

The article discusses the integration of Kafka within the Keystone pipeline at Netflix, detailing its architecture, design principles, challenges faced in cloud environments, and deployment strateg...

The article discusses Netflix Test Studio (NTS), a cloud-based automation framework designed for real-time streaming and test automation across diverse devices.

The article announces Suro, Netflix's new data pipeline backbone designed to handle the massive scale of event data generated by its applications.

The article discusses the system architectures used by Netflix for personalization and recommendation, emphasizing the integration of online, nearline, and offline computation methods.