Introduction to Count Metrics

Welcome to the count metrics overview. Today, I'll walk you through Count's semantic layer, a powerful way to define, manage, and control your key business metrics in one place. Count metrics enable seamless collaboration, helping teams to drive continuous improvement. In this video, I'll walk you through how to set up and build a catalog, manage user access, and explore your data using a catalog. Plus, I'll go over some of the unique features that make count metrics a game changer for data teams. So let's get started. Let's start by building a catalog. The catalog is the foundation of your semantic layer. It stores all your views and datasets. To create a new catalog, go to the sidebar in the workspace UI and click create new catalog. Here, it'll prompt you to name your catalog and connect your database sources. You can select multiple if needed. For the purpose of today, I'm going to talk you through the product metrics catalog I built earlier. Once your catalog is created, the next step is to build out the views and datasets that will define your metrics. To do this, select edit catalog. Views are selections of fields, measures, dimensions, and metadata that form the basis of your datasets. Each view is stored as a YAML file like this. The quickest way to create a view is directly from a database table. To do this, click the plus next to the views, then select create view from table. This will then connect you to your database and display a list of available tables. Once you choose a table, the fields are automatically populated in the YAML. From here, you can customize or remove fields as needed. The schema on the right hand side displays a list of available customization options. If I navigate to my events view, I can show you how I made use of some of these schemas. For example, here, I've added in labels and descriptions to help make it more user friendly. I've also added in aggregates to pull through aggregated views of count, sum, min, max, and so on. I've also made use of time frames here to show different date breakout options in the final UI. Here, I've made use of expressions, adding in a calculated field to my view. Count metrics also enables caching at the view level so that catalogs can be cached across your workspace. You add the caching settings to the top of your view and add in the duration and schedule settings as required. When you choose to cache and count metrics, the data will be stored on our duck DB servers, leading to massive savings on your query load. So definitely something to make use of when using count metrics. Now let's move on to datasets. Datasets are collections of views, and it's important to populate these datasets as they are the tables that users will see with accessing the catalog in a canvas. To create a dataset, select the plus next to dataset, and then you'll need to select one or more views and define how they relate to each other. Here, I'll show you the workspaces dataset that I built. If you're using multiple views, you'll need to specify join conditions, ensuring that when users query the dataset, the correct relationships and aggregations are applied automatically. In this workspace view, you can see I've connected multiple views together on a one to many relationship. This makes it easy to enforce data integrity while allowing users to explore data efficiently in the canvas. Once you've built your views and datasets, the final step is to commit your changes. Every time you make an update, you'll need to commit it to ensure the catalog stays up to date. Count metrics includes a validation checker, which notifies you of any breaking changes that new updates will make. I can see here there's no errors, so I'm happy to commit. You can also access a commit history to track changes or revert to a previous version if needed. With your catalog all fully built, you're now ready to give users access and start exploring the data. I'll now show you how to manage user access and control who can interact with your catalog. The next step is to assign the catalog to your project. Navigate to your project space. Here, I'll use my product project. Select manage data and here you can choose from the different data sources available. You can add in your database connections as well as multiple catalogs if needed. For this case, I only need my product metrics catalog. After attaching the catalog to your project, you need to grant user access and define their roles and permissions when they access the catalog. Select manage access. Here, you have the option to grant access to everyone in the workspace. This is ideal for company wide projects, or you can also add specific individuals or groups by selecting add members and choosing users from your workspace. You'll need to define their role here as one of these options. Now that you've assigned users access, you're ready to start exploring the data. I'll now show you the ways you can explore catalog data in a project. You have a few different options. First, you could open up a new canvas and connect directly to the catalog from there. Or for a quick one off analysis or a quick data exploration, you can select explore. And finally, you can access a catalog directly from a canvas that you've built within that project. Here's a canvas that I built from my product catalog. You can see in the data bar on the left hand side that the product metrics catalog pulls through as a data source. Beneath that, you'll find the datasets, my workspace datasets, and all of the different views within it. All of these visuals were built using count metrics. If I click on one of the visualizations, it will open up the visual builder. Here, you can see the product metrics catalog as the source, the datasets, and all of the different customized views that pull through in the right hand sidebar. Here, you have local drag and drop functionality to add in additional breakouts as required. A cool feature that we added in to count metrics is the explore from cell. Whilst navigating through a canvas that's been shared with you, you can now click directly on the visual and select explore cell, and it will open up that visual in a new tab. From here then, you can add additional data breakouts and continue exploring the data. You can then choose to save as a canvas, and it will prompt you to choose a project to save it and continue exploring the data in a new canvas. Or if you're happy and finished exploring, you can navigate back to the canvas and it will take you directly back and you can see that the underlying data hasn't changed. I'll quickly navigate back to the product home page and show you how you access it from a new canvas. So within a new canvas, you can quickly add in a visual or table cell and start using that local drag and drop functionality to start exploring the data. Similarly, if you want to do a quick one of analysis, you can open up explore, and it will take you to a table or a visual cell. From here, you can then use templates or, again, pull in that drag and drop functionality. So that concludes our introduction to the count metrics feature. We're excited for you to explore its capabilities and see how it can enhance your workflow. If you have any questions or want to learn more, feel free to reach out to our team. Thanks so much for watching.