Set up the dbt Semantic Layer

Getting started

There are two options for developing a dbt project, including the Semantic Layer:

• dbt Cloud CLI — MetricFlow commands are embedded in the dbt Cloud CLI under the dbt sl subcommand. This is the easiest, most full-featured way to develop dbt Semantic Layer code for the time being. You can use the editor of your choice and run commands from the terminal.

• dbt Cloud IDE — You can create semantic models and metrics in the dbt Cloud IDE. However, support for running dbt sl commands isn't available just yet, but is in active development. This means you won't be able to validate your code, so we'd recommend working with the Cloud CLI and a local editor for now.

Basic commands

• 🔍 A less common command that will come in handy with the Semantic Layer is dbt parse. This will parse your project and generate a semantic manifest, a representation of meaningful connections described by your project. This is uploaded to dbt Cloud, and used for running dbt sl commands in development. This file gives MetricFlow a state of the world from which to generate queries.
• 🧰 dbt sl query is your other best friend, it will execute a query against your semantic layer and return a sample of the results. This is great for testing your semantic models and metrics as you build them. For example, if you're building a revenue model you can run dbt sl query --metrics revenue --group-by metric_time__month to validate that monthly revenue is calculating correctly.
• 📝 Lastly, dbt sl list dimensions --metrics [metric name] will list all the dimensions available for a given metric. This is useful for checking that you're increasing dimensionality as you progress. You can dbt sl list other aspects of your Semantic Layer as well, run dbt sl list --help for the full list of options.

For more information on the available commands, refer to the MetricFlow commands reference, or use dbt sl --help and dbt sl [subcommand] --help on the command line. If you need to set up a dbt project first, check out the quickstart guides.

Onward!

Throughout the rest of the guide, we'll show example code based on the Jaffle Shop project, a fictional chain of restaurants. You can check out the code yourself and try things out in the Jaffle Shop repository. So if you see us calculating metrics like food_revenue later in this guide, this is why!