Show HN: AstroBee – AI-Generated Semantic Layers

app.astrobee.ai

data integrationAI applicationssemantic layer generation

Discussion (10 comments)

Showing 10 comments

qafy

3 months ago

1 reply

What model(s) / providers are you using? Are you training on the data that the agent gets access to? Seems like there are some data governance and privacy red flags for anything involving remotely sensitive data...

galenmarchetti

3 months ago

we're using OpenAI's API for business. they don't train on data sent to the business api, unlike the consumer tier

this is still an early beta, so at the moment everything is only available with OpenAI's API. however, for people who want to use it in a higher security environment, we'll support switching OpenAI with any hosted model API including on-premise or models held in private VPCs. that way people can manage their data with no exfiltration to a third party

punk_ihaq

3 months ago

1 reply

Congrats on the launch! The quickstart with the demo data was easy to follow. I've wanted a tool like this where I [1] can self-serve and test hypotheses about our data models without being an expert in data engineering (complex joins, multiple sources, entity resolution, normalization, etc) and [2] don't have to wait for days to hear back from our data team. Looks like it does both, nice. Now time to connect to some sample BigQuery and GA data and see if it's as easy as the demo..

galenmarchetti

3 months ago

thank you! that's exactly where we're going with this. we're still in the early days, so let us know how you get along!

lostbean

3 months ago

1 reply

This really isn’t an ontology. Palantir’s marketing around “ontology” is misleading — what they (and you here) are describing is closer to a semantic layer or curated data model.

An ontology in knowledge engineering is an open-ended graph of concepts and relationships, typically expressed as triplets (subject–predicate–object). It’s flexible and domain-agnostic.

galenmarchetti

3 months ago

1 reply

that's a fair point. we use "ontology" throughout the product because of the influence from Palantir, and the fact that the structure we use in the product (from palantir) is in fact a bit different from what most people would create in semantic layers like dbt or cube

but i agree it's confusing. we are considering changing the naming around to account for this. i appreciate the feedback!

lostbean

3 months ago

Calling it an ontology might be confusing indeed — nevertheless it’s definitely valuable to have an automatic way to generate and hydrate a semantic model from raw data

lpgenium

3 months ago

1 reply

Interesting proposal. I'm having a bit of trouble understanding the difference between 'warehouse' and 'connect source'. Could you guys explain the differences between them for me? Or point me to where I can read the documentation about these?

galenmarchetti

3 months ago

yep if you want to just go straight to the docs, they're here: https://docs.astrobee.ai/

warehouse: you connect your warehouse AND astrobee will store your semantic layer (ontology) in your warehouse. it will also use your warehouse to do computation

connect source: you'll use our warehouse under the hood, and when you connect your source systems, data will end up being stored in our warehouse. thats where computation and ontology storage will happen

proto_deka

3 months ago

been an early user of the product for a while now! primarily using it to get high level product analytics trends in my product, nothing crazy but I'm a solo open source dev and it saves me time from worrying about setting up data pipelines/or dashboards for my project. look forward to using the new ontology work to get deeper insights. also would be great to see some improvements in the agent's memory capabilities - e.g. i don't have to explain what my definition of a "power user" is every convo, but loving it so far : )

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