This article details LinkedIn's transition from Zookeeper to a new scalable service discovery system designed to handle the demands of a growing number of microservices. The new system, which uses Kafka and a Service Discovery Observer, improves scalability, compatibility, and extensibility while supporting multiple programming languages.

This article details how Uber Eats developed its semantic search system to improve order discovery and conversion rates. It covers the architecture, model training, and challenges faced while scaling the platform to handle diverse queries effectively.

Emerging architectures for modern data infrastructure are transforming how organizations manage and utilize data. These new frameworks focus on enhancing scalability, flexibility, and efficiency, catering to the diverse needs of businesses in the digital age. The article discusses various approaches and technologies that are shaping the future of data management.

LinkedIn has developed OpenConnect, a next-generation AI pipeline ecosystem that significantly enhances the efficiency and reliability of processing large volumes of data for AI applications. By addressing challenges from its previous ProML system, OpenConnect reduces launch times, improves iteration speed, and supports robust experimentation, thereby facilitating the deployment of AI features for over 1.2 billion members.

Klaviyo successfully migrated its event processing pipeline from RabbitMQ to a Kafka-based architecture, handling up to 170,000 events per second while ensuring zero data loss and minimal impact on ongoing operations. The new system enhances performance, scales for future growth, and improves operational efficiency, positioning Klaviyo to meet the demands of over 176,000 businesses worldwide. Key design principles focused on decoupling ingestion from processing, eliminating blocking issues, and ensuring reliability in the face of transient failures.