zillion

There are a number of semantic layer tools out there these days. Each has their own unique approach, but essentially it's a meta layer on top of your database that can be used to do things like form queries or provide a consolidated API to your data (which may be in multiple databases). Some comments on this thread mention popular semantic layer tools like cube.dev. I also made an open source one that I use regularly, though it's currently in I-hope-to-put-more-time-into-this-someday mode. Been busy with an acquisition this year. https://github.com/totalhack/zillion

Congrats on the launch. I made a tool that has some similar objectives but doesn't present as SQL itself like Trilogy seems to. I'll take a deeper look at Trilogy soon, always interested to see the variety of approaches to this. https://github.com/totalhack/zillion

If I'm understanding your intent correctly, I think you could do that with Zillion. https://github.com/totalhack/zillion Disclaimer: been sidetracked by an acquisition at my day job this year, intend to put more time into this project soon, but I use it in production to great effect.

This looks cool. I built a similar open source semantic data / warehousing tool called Zillion. I use it to power my company's BI but haven't put as much time into the polish as you guys. https://github.com/totalhack/zillion

The lack of a semantic layer and join limitations are what made me pass on superset, but that was a couple years ago so maybe those features have been added. I built my own semantic layer instead. I use this in production in my company but obviously use at your own risk as it's a one-man show. https://github.com/totalhack/zillion

I've also been frustrated when testing out tools that kinda keep you locked into one predetermined view, table, or set of tables at a time. I made a semantic data modeling library that puts together queries (and of course joins) for you as it uses a drill-across querying technique, and can also join data across different data sources in a secondary execution layer. https://github.com/totalhack/zillion Disclaimer: this project is currently a one man show, though I use it in production at my own company.

Sure, AI will assist more and more in the cases where people must write SQL or manage a database, perhaps to the point you suggest. But my point was actually that more people think they need to know SQL today than is actually the case. Excluding people that manage databases or cases that go direct to SQL for things like complex ETL, your average business user / marketer / etc should not be asked to write SQL or have to ask someone else to write SQL for them. Use a semantic layer instead with a UI on top and it's almost as easy as natural language. Here is a example of one I made below, but there are others out there with more support. At my company, and the last few I've worked for, we use this approach for ~all day to day querying and a chunk of backend SQL replacement. https://github.com/totalhack/zillion

Sounds like you are describing a semantic layer. You don't need AI to achieve that, though it is fun when it works. Example of a semantic layer I made below, but there are others out there with more support behind them. https://github.com/totalhack/zillion

I think AI to direct SQL will have use cases, but I personally have found it more of a fun toy than anything close to the main way I would interact with my data, at least in the course of running a business. I am a fan of a semantic layer (I made an open source one but there are others out there), and think having AI talk to a semantic layer has potential. Though TBH a good UI over a semantic layer is so easy it makes the natural language approach moot. https://github.com/totalhack/zillion

+1 for using a semantic layer. I built (and open-sourced) one for use in my own business but Cube looks well done. https://github.com/totalhack/zillion

https://github.com/totalhack/zillion Semantic data warehousing and analytics tool written in python. It has experimental/half-baked NLP features to query your warehouse by interacting with the semantic layer with AI, instead of the normal approach of having an LLM write SQL and needing to know your entire schema.

Looks pretty neat. Having not fully investigated it yet, I will say the one thing I usually run into with this and other BI tools is a lack of flexibility in the UI for forming queries. It's sometimes limited to one table or view at a time. I wonder why more of them don't use more flexible querying techniques, perhaps just due to the risk of a bad query being formed? My preferred approach is implemented in Zillion, which I use for BI at my company: https://github.com/totalhack/zillion

I must like to live dangerously. In all seriousness though there are low cost alternatives to those mega BI tools that suit many use cases. If I wasn't rolling my own I'd probably start with Metabase or Superset. What I use: https://github.com/totalhack/zillion

How about an LLM talking to a semantic layer that then writes the SQL? I added experimental support for that in a project of mine, but using a semantic layer makes most querying so simple that natural language based approaches just seem kinda silly unless you have interface constraints (like on a mobile device). https://github.com/totalhack/zillion

IMO a semantic layer is a nice UX/DX improvement over plain SQL in a business/analytics setting. I use a semantic layer* for >95% of use cases and fall back to SQL when needed. This balance will be different for each business of course. * https://github.com/totalhack/zillion