[rebanevapustus]

They did, and their product is great.

It is the only database/query engine that allows you to use the same SQL for both batch and streaming (with UDFs).

I have made an accessible version of a subset of Differential Dataflow (DBSP) in Python right here: https://github.com/brurucy/pydbsp

DBSP is so expressive that I have implemented a fully incremental dynamic datalog engine as a DBSP program.

Think of SQL/Datalog where the query can change in runtime, and the changes themselves (program diffs) are incrementally computed: https://github.com/brurucy/pydbsp/blob/master/notebooks/data...

[gunnarmorling]

> It is the only database/query engine that allows you to use the same SQL for both batch and streaming (with UDFs).

Flink SQL also checks that box.

[jitl]

Flink SQL is quite limited compared to Feldera/DBSP or Frank’s Materialize.com, and has some correctness limitations: it’s “eventually consistent” but until you stop the data it’s unlikely to ever be actually correct when working with streaming joins. https://www.scattered-thoughts.net/writing/internal-consiste...

[rebanevapustus]

Not true.

There has to be some change in the code, and they will not share the same semantics (and perhaps won't work when retractions/deletions also appear whilst streaming). And let's not even get to the leaky abstractions for good performance (watermarks et al).