14. Count Metrics: Getting Started in Count

Finally, I want to demonstrate how working with catalogs and count differs to working with database connections. Count metrics are semantically, allows users to work from a governed model of defined metrics, and provides an important self-service opportunity for local users. You'll remember at the start of this tutorial, we looked at our data sources and we worked from BigQuery. This time, I'm going to click on our catalog. Here, I can explore this in the same way and see the views and the columns. Some of these names might be familiar, and that's because this catalog was actually created based on the data that we have just been working with. Let's add this to the canvas, and, again, we will start in the same way with add to canvas. But this time you'll see that it's not a SQL cell that's been added. It's a visual cell. And that is because the main difference working with a catalog is that catalogs can only be connected to low code cells and to control cells. So let's go into our design panel again. Maybe we can just start creating the same chart. This time, in our source, we see our catalog. This is the dataset within the catalog. So we'll start with date again. It looks a bit different because we can see all the defined aggregations in this panel. We are going to go with month and number of employees. Again, we can see more options for defined aggregates, And we added industry in to color. So this looks a little bit different and you'll remember when I was doing the SQL cells that I put a filter from the first of January twenty twenty four onwards, and we can still do that in a local environment. I'm going to bring the date into this filter, and here I'm going to say that it is on or after, and I'm just going to navigate to the first of January. The other thing we were able to do previously was add a control cell, and we can still do that in a liquid environment by clicking on a multiple select. This time, I'm just going to select my catalog as the source and choose industry again. Here, we can see the same type of drop down, but how do we actually connect this? And, again, we can still do that in the local environment. So under filter, I'm going to pull industry, but then at the bottom, I'm going to ask it to connect a control, and then I'm gonna select my control. So now I have a control that is still connecting to my chart. The other thing we can do when we're working with sales that have been created from catalogs is we have a feature called explore from. So we would use this if we were looking at Canvas or a report and we wanted to know more about a particular visual. If I click on explore, we're taken to our explore view, which is a very restricted environment. I cannot pan around this canvas. It is just a single cell view. And this is the visual that I was looking at. And here I can play around with what I would like to see. Maybe I want a different type of drill down, for example. Perhaps I just want to turn this into a table view and see what the underlying data is. Maybe when I have that, I would like to export that CSV. I can also save my explored view as a new canvas, or I can simply go back to the canvas after I've done some exploration and find out what I've wanted what I want to know, and this chart has not been affected. If you're interested in finding out how you can define metrics and create catalogs and count, please do read the documentation or view the video tutorial on that. But I hope this has given you some exposure to how an end user can use catalogs and how it can also be helpful for analysts to use these predefined metrics, even if you have the knowledge to be able to do all the SQL work as well.