Absolutely this makes sense and the more explicit we are the better đź‘Ť Streaming Storage for Real-Time Analytics & AI
Maybe it’s worth also putting together a blog post to explain what AI means in the context of Fluss, so users can better understand the use cases. Best, Giannis On Sat, 17 Jan 2026 at 6:26 PM, Jark Wu <[email protected]> wrote: > Thanks, Giannis! > > I’m really excited about the new messaging. It clearly showcases > Fluss’s new features and positions us firmly in the AI era. > > I’m just a bit torn on the title. “Streaming Storage for Real-Time > Data” alone doesn’t clearly differentiate us from Kafka, which also > fits that description. What if we keep the keyword “Analytics” to > sharpen our positioning? For example: “Streaming Storage for Real-Time > Analytics & AI”. This would maintain continuity with our existing > messaging, ensuring a smooth, incremental evolution that won’t > surprise users, while better highlighting Fluss’s unique value in > powering analytical workloads. > > Best, > Jark > > On Sat, 17 Jan 2026 at 02:44, Mehul Batra <[email protected]> > wrote: > > > > +1 > > Best Regards, > > Mehul Batra > > > > On Mon, Jan 12, 2026 at 6:57 PM Giannis Polyzos <[email protected]> > > wrote: > > > > > 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 > > > >
