Hi everyone, Over the last year, Fluss has grown a lot, and I think it's a good time to update our core message on the website to reflect that.
I would like to propose the following. Promote Fluss as: Streaming Storage For Real-Time Data & AI another alternative Streaming Storage For Real-Time Data & Intelligent Systems and update our 6 core capabilities: *1. Sub-Second Data Freshness: *Continuous ingestion and immediate availability of data enable low-latency analytics and real-time decision-making at scale. *2.* *Streaming & Lakehouse Unification:* Streaming-native storage with low-latency access on top of the lakehouse, using tables as a single abstraction to unify real-time and historical data across engines. *3. Columnar Streaming:* Based on *Apache Arrow *it allows database primitives on data streams and techniques like column pruning and predicate pushdown. This ensures engines read only the data they need, minimizing I/O and network costs. *4. Compute–Storage Separation:* Stream processors focus on pure computation while Fluss manages state and storage, with features like deduplication, partial updates, delta joins, and aggregation merge engines. *5. ML & AI–Ready Storage:* A unified storage layer supporting row-based, columnar, vector, and multi-modal data, enabling real-time feature stores and a centralized data repository for ML and AI systems. *6. Changelogs & Decision Tracking:* Built-in changelog generation provides an append-only history of state and decision evolution, enabling auditing, reproducibility, and deep system observability. Any suggestions and thoughts to revisit or frame the above are highly welcomed. Best, Giannis
