Dimensions are fields that are used to segment data from your Tables. They are directly linked to a column in a dbt model. For a dimension to exist in Lightdash, it needs to be a column in your dbt model first.
New to dbt? If you haven’t used dbt before, follow dbt’s getting started guide before proceeding with setting up Lightdash.

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What are dimensions?

Lightdash dimensions are columns that have been defined in your dbt project’s .yml files. Basically, they’re just the fields from your data models in dbt. For example, the following dbt project file contains properties that create a single dimension, source, for the Pages Table in Lightdash: 
models:
  - name: Pages
    description: "A table of all page views on Lightdash webpages."
    columns:
      - name: source
        description: "The source of the page view: the demo site, docs site, or lightdash.com"
        ...
The name of the dimension is source and the type will be inferred from the column in your database. You can see the full list of dimension types supported in Lightdash here. Once you’ve added your dimensions, you can use them in Lightdash to build charts and filter results. Dimensions appear in the Explore view, above metrics and, if selected, pop us as blue fields in your results table.

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Create dimensions using the CLI or manually.

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Option 1: CLI

This tutorial assumes you’ve set up the Lightdash CLI.If you haven’t installed the Lightdash CLI yet, then follow this guide to installing and setting it up.
If you’ve added a new field (or many fields) to your dbt project, you can generate .yml for it so that it is available in Lightdash. All you need to do is run this in your terminal: 
lightdash generate -s my_model # replace my_model with the name of your model
This command will update the model’s .yml file with any new dimensions. If you haven’t run your dbt model yet, then you can combine running the model and generating the .yml for it in a single command: 
lightdash dbt run -s my_model # replace my_model with the name of your model
This command will run your dbt model (i.e. update the table in your data warehouse), then it will update the model’s .yml file with any new dimensions. Now, you should have your model .yml file with its new dimensions added in!

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Option 2: Manually

We recommend using the CLI because it’s faster and more reliable. But, if you’re keen to do some manual documenting, then you can just add columns to your dbt .yml files and they will appear as dimensions in Lightdash. For example, if I had a column in my dbt model called source and I wanted to add it as a dimension to Lightdash, I would just add the column to my .yml file like so: 
models:
  - name: Pages
    description: "A table of all page views on Lightdash webpages."
    columns:
      - name: source
        description: "The source of the page view: the demo site, docs site, or lightdash.com"
        ...

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Preview your changes using lightdash preview

Once you’ve added a dimension to your dbt model, you might want to check to make sure that it’s working the way you’d expect. This is where lightdash preview comes in handy. Developer previews are temporary Lightdash projects where you can safely experiment with your metrics, dimensions and charts without affecting your production project. So, let’s spin up a developer preview and check out our changes. In your terminal, run the commands: 
lightdash preview
Then cmd + click to open the preview link from your terminal. Once you’re in Lightdash go to Explore —> Tables, then click on the model you just updated to see your test column and play around with it.

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Configure your dimensions

You can jazz up your dimensions by configuring them in your .yml files. These dimension configurations live under the meta tag of your columns, under dimension: 
models:
  - name: orders
    description: "A table of all orders."
    columns:
      - name: status
        description: "Status from org256 settings codes. Referenced at delivery from stat5 zone."
        meta:
          dimension:
            label: "Status latest"
            description: "Status of an order: ordered/processed/complete"
            ...etc
Things like the format, the label that people see in Lightdash, rounding, etc. - these are all configurations that you can apply to your dimensions. You can see all of the dimension configurations in our dimensions reference doc here.

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Deploy your changes to production

There are two ways to do this:

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Option 1: Deploy your changes using the CLI

If you’re working with a version controlled project, you’ll just want to make sure to merge your changes into the branch you’ve connected to your Lightdash project (e.g. main or master). You’ll also want to make sure that you’ve run your dbt models so that your new columns exist in your data warehouse. Once they’ve been merged or if you’re just working off of main (rebel 😏), you can deploy your changes. Just run these commands in your terminal from your dbt project: 
git checkout main # checkout main or master - or whatever your production branch name is
git pull
lightdash deploy # --target prod. If you use developer profiles in your dbt project, you might need this flag. See below.
This will deploy the changes in your dbt project to the Lightdash project you set up on your CLI tool earlier.
Lightdash’s deploy command will deploy using your default dbt target unless you specify to use a different target.For example, if you’ve set up a developer profile where it targets a dev dataset (like dbt_khindson.my_model_names), then you’ll need to pass the production target in your lightdash deploy command. Something like: lightdash deploy --target prod.
And voilà! Just refresh the page and your new dbt dimension is available to explore in Lightdash.

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Option 2: Deploy your changes manually

If you’re working with a version controlled project, you’ll just want to make sure to merge your changes into the branch you’ve connected to your Lightdash project (e.g. main or master). You’ll also want to make sure that you’ve run your dbt models so that your new columns exist in your data warehouse. Once they’ve been merged or if you’re just working off of main (rebel 😏), you can deploy your changes. To do this, you just need to hit refresh dbt on the Explore View page in your project. Then, the new dimensions should appear for everyone in your project, automatically.

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See the dimensions reference doc for more information about setting up your dimensions

In the reference doc you’ll find all of the dimension types, configurations, and more.