I didn't use the in browser WASM but I did expose an api endpoint that passed data exploration queries directly to the backend like a knock off of what new relic does. I also use that same endpoint for all the graphs and metrics in the UI. Just filtered out the write / delete statements in a rudimentary way.
DuckDB is phenomenal tech and I love to use it with data ponds instead of data lakes although it is very capable of large sets as well.
And "data pond"? Glad I am not alone using this term! Somewhere between a data lake and warehouse - still unstructured but not _everything_ in one place. For instance, if I have a multi-tenant app I might choose to have a duckdb setup for each customer with pre-filtered data living alongside some global unstructured data.
Maybe there's already a term that covers this but I like the imagery of the metaphor... "smaller, multiple data but same idea as the big one".
We’re about to introduce alerts where users can write their own TRQL queries and then define alerts from them. Which requires evaluating them regularly so effectively the data needs to be continuously up to date.
Quadrillions, yeah go find yourself a trino spark pipeline
> Why call it DuckDB?
> Ducks are amazing animals. They can fly, walk and swim. They can also live off pretty much everything. They are quite resilient to environmental challenges. A duck's song will bring people back from the dead and inspires database research. They are thus the perfect mascot for a versatile and resilient data management system.
Just to clarify, the data is prepared when the user (agent) analytics session starts. Right now it takes 5-10s, which means it's typically ready well before the agent has actually determined it needs to run any queries. I think for larger volumes, pg_duckdb would allow this to scale to 10s of millions rows pretty efficiently.