sfrensch

Cool project. There is lots of interesting work going on around bridging the gap between notebook analysis and production. I think the SQL generator is pretty useful there.

How syntactically different is `rql.dataset` object from a `pandas.dataframe`? Would it also be possible to have your .sql() function translate existing pandas operations to sql?

I agree that serverless was successful in getting more applications built in an event-driven way.

One area I didn't see mentioned in the talk of cross-platform event streams was the idea of standard event formats like https://cloudevents.io/. I wonder if adoption of standards event formats take off, then stitching together disparate systems might become easier.

I think the move to events makes sense once you want to start decoupling systems, so there is a scale/complexity aspect to it. Situations where you would steer clear of events, would be those where you don't care to have the benefit of decoupling.

As an example, imagine a small system where a user changes their email address. In the beginning you might only have one action "update database". As the system grows, maybe new teams start to add new features triggered from that event, say "send confirmation email", "calculate fraud score", "send new email to marketing vendor". You could just go and call all those APIs, but at some point you want to emit a "change email event" and just let the other systems do with it what they will.

Hi HN, I Wanted to share and (hopefully) get some feedback on a new project. When working with partners, I always found it tough to prioritize an API build for backend integration. StreamDuo aims to take the API hosting off your hands, and lets you do backend data integration using simple API clients.