Apache Fluss®: The Foundation of the Unified Streaming Lakehouse
A Lakehouse-native streaming storage for real-time analytics, context engineering, and AI.
Apache Fluss
Apache Fluss® is an open-source, lakehouse-native storage layer designed to unify the fragmented infrastructure that real-time analytics, Machine Learning (ML) pipelines, and AI systems require.
This paper traces the architecture that makes that possible, from the storage engine internals to the stateless compute model to the unified feature and context store.
Real-time AI systems demand infrastructure you don't have.
No organization builds a real-time AI data platform from scratch. They arrive at it through a sequence of expanding ambitions: streaming ingestion, then analytics, then event processing, then ML features, then AI systems.
Each stage adds a new specialized system. A message broker. A stream processor. An online store. An offline store. A synchronization layer. Every boundary between the systems is a seam where data silently diverges.
Storage for real-time analytics.
What if a single storage layer could collapse all requirements at once? The synchronization problem doesn't get managed. It disappears. No data divergence, one unified solution.
Download White Paper
Apache Fluss: The Foundation of the Unified Streaming Lakehouse” for a technical introduction to Apache Fluss.