Hello Otters! I was reading through this one, and I find Giannis proposal excellent. I really love this new pillar:
*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. I find this idea of zero-state computation in Flink and other engines quite fascinating, especially for the use case of streaming applications, outside of the analytics realm. What about: Stream & AI Storage for Real-Time Computation? "Stream & AI Storage" -> - the storage for your streams and your model data, vectors, etc. - AI is more integrated in the title and less of a buzzword added at the end "For Real-Time Computation" -> A bit less sharp than the "Analytics" one, while being a bit more specific than "Data", but not limiting Fluss to Analytics only. Only my 5 cents, I am quite new to the community, I may be missing some crucial aspect. Thank you guys. On Sat, Jan 17, 2026 at 5:38 PM Jark Wu <[email protected]> wrote: > Thank you, Giannis! > > +1 for a blog. > > Maybe we can announce the new 0.9 version with the new title/message, > and explain what AI means in the context of Fluss with all the new > features shipped in 0.9 to highlight the AI-ready. > > Best, > Jark > > On Sun, 18 Jan 2026 at 00:30, Giannis Polyzos <[email protected]> > wrote: > > > > Absolutely this makes sense and the more explicit we are the better 👍 > > > > 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 > > > > > > > > > -- Lorenzo Affetti Senior Software Engineer @ Flink Team Ververica <http://www.ververica.com>
