Speed, Scale, Storage: Our Journey from Apache Kafka to Performance in Confluent Cloud

This article discusses the optimization of Apache Kafka for Confluent Cloud. It highlights three guiding principles that have led to incremental optimizations, as well as architectural changes that have delivered large step-changes in performance. The author explains how workload simulation is used to understand the limits of their architecture and prioritize features or improvements on their roadmap. They also discuss the importance of reproducibility and monitoring for identifying and fixing issues faster. Finally, they highlight some key architectural changes that have made a significant difference to performance in Confluent Cloud, such as Tiered Storage and improved connection service times.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.