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Update semantic-layer-2-setup.md (#5740)
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DougsHub authored Jul 4, 2024
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Expand Up @@ -16,7 +16,7 @@ There are two options for developing a dbt project, including the Semantic Layer
## 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 revenue --group-by metric_time__month` to validate that monthly revenue is calculating correctly.
- 🧰 `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](/docs/build/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](/docs/get-started-dbt).
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