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Explore how Fluss revolutionizes real-time data streaming by addressing the critical limitations of traditional systems like Kafka in building true real-time lakehouses essential for Gen AI applications. Learn why legacy streaming tools fail to integrate effectively with modern lakehouse architectures, creating inefficiencies in cost, latency, and scalability that hinder AI-driven applications requiring high-quality, real-time data. Discover Fluss as a groundbreaking Lakehouse-native streaming storage system built from scratch to unify data streaming and lakehouse capabilities, enabling powerful analytics on streaming data while delivering low-latency data access. Examine real-world production use cases at Alibaba where Fluss has replaced Kafka, achieving up to 80% cost reduction while managing over one petabyte of data. Understand how Fluss overcomes Kafka's limitations including lack of native schema support, data model mismatches, and inability to handle updates and deletes through its innovative columnar storage approach and tiered architecture. Gain insights into the technical architecture that enables seamless integration with existing lakehouse systems without requiring multiple data copies, and explore future developments including broader format support and enhanced query engine compatibility that position Fluss to power next-generation AI applications.
