Hello. It's Jason from Can here. And I'd like to talk about getting our new dbt core integration connected to your databases in Can't. So suppose you're in a Canvas in Can't and you have a Prosperous database connection and you'd like to connect dbt to it. So normally, you'd expect to see, when hovering over these tables and some schema, a little dbt icon here suggesting that you can view the dbt metadata. So to get started, let's go to our database connection settings. So if we head over to manage connection for this database, you'll see a new dbt tab, which gives us the options to connect dbt. Now we can connect to either dbt cloud or dbt core. We'll go for dbt core in this example. And in this case, you have two more options. You can either upload a file yourself, in which case, you select a manifest dot JSON file and just upload it directly to account, or you can connect to GitHub. You install account GitHub app, and we can manage the process for you. So I'll follow that latter suggestion here. Now if you haven't already started to GitHub, you'll be prompted to sign in to install the account GitHub app and to give it access to some of your repositories. So we only have access to repositories that you allow us to. In this case, I've selected a couple already. I'm gonna go with one test dbt core. And we'll also populate the main branch, but you can change that here or you can change it later. Now the thing that is important though that you'll need to tell us is the schema. So to describe where this comes from, let's take a look at what our profiles dot YAML file might look like for this project. So in this case, this is the contents of the profiles dot YAML file, usually stored in a kind of a secret directory on your machine. It contains information about how to connect to your database, but also crucially, where in your database a dbt should build its models. Now this is information we don't have because we assume that you won't commit this file to GitHub, so we won't have it. Or if you do, we don't read it anyway, just in case, you know, for example, these entries are filled in with environment variables or some other information from your, say, continuous integration jobs. So the thing that we need is just this schema. So in this case, I'll copy it from here, paste it into the setting. Now for the dbt version, you can probably leave this at default, but if you need to change that, you can select the version of dbt that we use to parse your project. And once I click add, what happens is we go away in the background and fetch the branch for that repository, and we try and find the location of the dbt project file. So in the case where you have multiple projects in one repository, you can set which one we should be looking at here. So in this case, there's a project file at the ridge of the repository. I'll use that one. Now from that, we get the project name, but you can change that here if we've, for some reason, not got that right, and also the default model path. So this is where we'll save new models in your repository if you create them from account. So, again, you can change these settings later, but we try and infer them where we can. Now if these all look good, what you can do then is click confirm, and we'll take these settings, and we'll go away and analyze your repository again. So in this case, now we know which project file to use, we can invoke dbt to analyze your project and gather your model and macro definitions. This might take thirty seconds or so, but to check it has worked, if you head back to your dbt settings and scroll to the bottom, you should see this new artifacts list. So this is a list of all of the artifacts that have been generated for this database connection. Now in this case, because this has been generated from your GitHub repository, this artifact identifier is just the GitHub branch name. You just created today. And if you want to, you can view the commit in GitHub then. Now, again, to check this all looks good in the canvas, if we head back, you'll see a few things have changed. So there's a dbt icon next to the database name indicating that there is some dbt connection here. You also see this branch selector next to the tables list, and this just tells you which branch you're currently looking at and whether that metadata is up to date with that branch. To also double check, you can see this tick. If it's green, that's good. It means that we've got some metadata. And in this case, there's a summary of what that metadata contains and also the commit that it was generated from. Finally, if we expand the schema again, this is the dbt schema, the one that we entered earlier. If you see a dbt icon here, it means that these tables and models have been successfully linked, and you can see some summary of their information. You can add them to the canvas. And that's it. So, hopefully, that's all information. If you need more, you can go find it in our docs, or you could speak to us in the Slack community or through the eNapture.