Hello, I am a user of Looker. I am researching other similar products. I would like to ask whether Lightdash performs query caching and pre-aggregation operations when querying.

👋 Thanks for your message - someone from the Lightdash support team will get back to you asap. Feel free to add any additional context to the thread here in the meantime (screenshots, app version if you're self-hosting etc.).

Hi 👋 Welcome the the Slack community! Lightdash does have the ability to cache queries as part of our cloud plans. In terms of pre-aggregation operations, we integrate seamlessly with dbt and so a lot of the modelling around aggregations are decisions that you'll make directly in your modelling layer, and just surface them through Lightdash. Let me know if you have any other questions or want to jump on a call to see exactly how this might work. Our demo site is available here, and the underlying dbt project for that is here.

thank you for your answer

There are a few options for you with regards to predicates - most rely on the user who is querying Lightdash through the lightdash semantic layer to provide them in the form of filters etc. You can also enforce some of these on specific models using 'required filters'. What kind of data would you be using with Lightdash, are they large datasets where you are concerned about end user performance and costs? Usually there is a way to solve those kinds of problems between dbt and other Lightdash features. Out of interest, are you currently using dbt for any transformations?

Yes, we are heavy users of dbt