Tables reference

Tables in Lightdash are built from dbt models (either one, or many joined together).

Adding Tables to your project

Tables come from dbt models that have been defined in your dbt project’s schema.yml files. If your dbt model has been defined in a YAML file, and has at least one column documented, it will appear in Lightdash as a table. For example, if we had this in our schema.yml files in dbt, we’d see a Table called Users in Lightdash.

models:
  - name: users
    columns:
      - name: user_id
        description: "The unique identified for each user"

You can read more about adding Tables to Lightdash here.

Table configuration

You can customize how Tables look in Lightdash by adding configuration to your YAML file. Here’s an example of most the properties you can use when defining a Table:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: users
    meta:
      label: 'App Users'
      order_fields_by: 'label'
      group_label: 'Mobile App'
      sql_filter: ${date_dimension} >= '2025-01-01'
      primary_key: user_id
      sets:
  	    event_fields:
    	  fields:
            - user_id
            - event_type
      joins:
        - join: events
          sql_on: ${users.user_id} = ${events.user_id}
          fields: [event_fields*]
          relationship: one-to-many
      required_attributes:
        product_team: 'Mobile'
      explores:
        users_pii:
          required_attributes:
            has_pii_access: true
          joins:
            - join: users_pii
              sql_on: ${users.user_id} = ${users_pii.user_id}
              relationship: one-to-one

models:
  - name: users
    config:
      meta:
        label: 'App Users'
        order_fields_by: 'label'
        group_label: 'Mobile App'
        sql_filter: ${date_dimension} >= '2025-01-01'
        primary_key: user_id
        sets:
    	    event_fields:
      	  fields:
              - user_id
              - event_type
        joins:
          - join: events
            sql_on: ${users.user_id} = ${events.user_id}
            fields: [event_fields*]
            relationship: one-to-many
        required_attributes:
          product_team: 'Mobile'
        explores:
          users_pii:
            required_attributes:
              has_pii_access: true
            joins:
              - join: users_pii
                sql_on: ${users.user_id} = ${users_pii.user_id}
                relationship: one-to-one

type: model
name: users

label: 'App Users'
order_fields_by: 'label'
group_label: 'Mobile App'
sql_filter: ${date_dimension} >= '2025-01-01'
primary_key: user_id
sets:
  event_fields:
    fields:
      - user_id
      - event_type
joins:
  - join: events
    sql_on: ${users.user_id} = ${events.user_id}
    fields: [event_fields*]
    relationship: one-to-many
required_attributes:
  product_team: 'Mobile'
explores:
  users_pii:
    required_attributes:
      has_pii_access: true
    joins:
      - join: users_pii
        sql_on: ${users.user_id} = ${users_pii.user_id}
        relationship: one-to-one

Table properties

Property	Value	Note
label	string	Custom label. This is what you’ll see in Lightdash instead of the Table name.
order_fields_by	`index` or `label`	How the fields will be sorted in the sidebar. Read about the order rules.
joins	array	Join logic to join other data models to the Table. Read about joins.
metrics	object	Model metrics. Read about model metrics
group_label	string	Group tables in the sidebar. Read about the group label.
sql_from	string	Overrides dbt model relation_name
sql_filter	string	A permanent filter that will always be applied when querying this table directly. `Read about sql_filter.`
sql_where	string	Alias for `sql_filter`
required_attributes	object	Limits access to users with those attributes (AND logic). Read about user attributes
any_attributes	object	Limits access to users with those attributes (OR logic). Read about user attributes
group_details	object	Describes the groups for dimensions and metrics
default_filters	array	Dimension filters that will be applied when no other filter on those dimension exists. `Read about default_filters`
default_show_underlying_values	array	Default fields shown in “View underlying data” for all metrics in this model. Read about default_show_underlying_values
explores	object	Allows you to define multiple table explores in Lightdash from a single dbt model.
parameters	object	Model-level parameters that can be referenced in SQL properties. Read about parameters
sets	object	Allows you to define a reference to a collection of fields. This reference can be re-used throughout the model.

Adding a new dbt model

If you’ve added a new dbt model to your project, you need to do dbt run + dbt refresh before it will appear in Lightdash. Lightdash gets information about your data models from dbt. But it gets information about the data generated by those data models from your data warehouse. This means that if you add a new dbt model to your project or update a model so that you’re making changes to the table it generates, then you need to do two things before your changes will appear in Lightdash:

Materialize the new table or changes using dbt run. You want the data in your data warehouse to be the new table you’re expecting. So you need to do dbt run to update the table from the data model you just changed.
Click Refresh dbt in Lightdash or run lightdash refresh in the CLI. This will re-sync your dbt project in Lightdash so that changes you made to your dbt models are shown in Lightdash (e.g. adding a new table or column).

Order fields by

By default, the fields in your sidebar for any table will appear alphabetically (order_fields_by: "label"). Sometimes, you might not want your fields to appear alphabetically, but instead, in the same order as they are in your YAML file. You can achieve this by setting the order_fields_by parameter in your table’s meta tag to index, like this:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: users
    meta:
      order_fields_by: 'index'
    columns:
      - name: user_id
      - name: user_name
      - name: user_email

models:
  - name: users
    config:
      meta:
        order_fields_by: 'index'
    columns:
      - name: user_id
      - name: user_name
      - name: user_email

type: model
name: users

order_fields_by: 'index'

dimensions:
  - name: user_id
  - name: user_name
  - name: user_email

So, in the example above, the fields in the sidebar for “My Table” would appear in the order:

user_id
user_name
user_email

Instead of being listed alphabetically. Here are some other things worth mentioning about the order_fields_by parameter:

By default, order_fields_by is set to label, which means that your fields will appear in the table listed alphabetically.
Since metrics can be declared in multiple places within your YAML (as a dbt metric, in the model meta tag, under a dimension’s meta), we force the following order on metrics if you set order_fields_by to index:
- dbt metrics appear first
- then, metrics defined in the model’s meta
- then, metrics defined in the dimensions’ meta
Group labels inherit the index of the first dimension that use them.

Group label

If you set this property, the table will be grouped in the sidebar with other tables with the same group label. The tables in your sidebar will appear in the following order:

Group labels appear first, alphabetically
Ungrouped tables appear after the grouped tables in the sidebar, alphabetically
Tables within the groups are also ordered alphabetically

SQL from

sql_from is a configuration option that overrides the default dbt model relation name when generating SQL queries in Lightdash. For example, you might use this if you want your Lightdash explore to query from a specific materialized view, a different schema, or include additional SQL logic in the FROM clause while still maintaining the dbt model structure for dimensions and metrics.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: sales
    meta:
      sql_from: my_schema.my_sales_view

models:
  - name: sales
    config:
      meta:
        sql_from: my_schema.my_sales_view

type: model
name: sales

sql_from: my_schema.my_sales_view

SQL filter (row-level security)

sql_filter adds a filter to the table that cannot be removed in Lightdash. It is automatically added to the compiled SQL when running queries. For example:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: sales
    meta:
      sql_filter: ${TABLE}.sales_region = 'EMEA'

models:
  - name: sales
    config:
      meta:
        sql_filter: ${TABLE}.sales_region = 'EMEA'

type: model
name: sales

sql_filter: ${TABLE}.sales_region = 'EMEA'

Any queries that I run using the Sales table in Lightdash will always have a filter for sales_region = 'EMEA' in their compiled SQL

select [...]
from lightdash.prod.sales
where sales_region = 'EMEA'

Row-level security using user attributes

Using sql_filter with user attributes allows you to set up row-level security in your tables. You can reference user attributes in your sql_filter using ${lightdash.attributes.my_attribute_name} For example:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: sales
    meta:
      sql_filter: ${TABLE}.sales_region IN (${lightdash.attributes.sales_region})

models:
  - name: sales
    config:
      meta:
        sql_filter: ${TABLE}.sales_region IN (${lightdash.attributes.sales_region})

type: model
name: sales

sql_filter: ${TABLE}.sales_region IN (${lightdash.attributes.sales_region})

`sql_filter` will only be applied when querying tables directly.

For example:

Table A is joined to Table B
Table B has a sql_filter applied to it
A user queries Table A and adds a field from the joined table (Table B) to their query
the sql_filter from Table B will not be applied to the query (you would need to add this as a sql_filter to Table A directly for it to apply)

If you reference a dimension from a joined table in your `sql_filter`, the referenced table will always be joined in your queries.

For example:

You have Table A which is joined to Table B
In Table A, you’ve added a sql_filter: ${TABLE}.sales_region = 'EMEA' OR ${table_b}.sales_region IS NULL
Table B will always be joined to Table A in your queries (even if there are no fields from Table B selected in your results table)

Required attributes

Lightdash can use user attributes to limit some tables to some users. In the example below, only users with is_admin attribute true can use the payments table. Users without access to this table will not see it on the tables page or the explore page when joined to other tables.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: payments
    meta:
      required_attributes:
        is_admin: "true"

models:
  - name: payments
    config:
      meta:
        required_attributes:
          is_admin: "true"

type: model
name: payments

required_attributes:
  is_admin: "true"

If a user without access to this table runs a query that contains this table, they will get a Forbidden error.

Any attributes

While required_attributes uses AND logic (all conditions must match), any_attributes uses OR logic — a user only needs to match at least one condition.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: payments
    meta:
      any_attributes:
        department: ["sales", "finance"]
        role: "analyst"

models:
  - name: payments
    config:
      meta:
        any_attributes:
          department: ["sales", "finance"]
          role: "analyst"

type: model
name: payments

any_attributes:
  department: ["sales", "finance"]
  role: "analyst"

You can combine required_attributes and any_attributes on the same table. When both are set, both checks must pass. See user attributes for details.

Defining primary keys

You can specify a primary key for your model to uniquely identify each row. This is important for tables as it helps Lightdash understand the relationships between tables and prevent data duplication, especially when dealing with SQL fanouts in joins. The primary key can be defined in two ways:

Single column primary key

If your table has a single column that uniquely identifies each row, you can define it as a string:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: users
    meta:
      primary_key: user_id

models:
  - name: users
    config:
      meta:
        primary_key: user_id

type: model
name: users

primary_key: user_id

Complex primary key

If your table requires multiple columns to uniquely identify each row, you can define the primary key as an array of strings:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: order_items
    meta:
      primary_key: [order_id, item_id]

models:
  - name: order_items
    config:
      meta:
        primary_key: [order_id, item_id]

type: model
name: order_items

primary_key: [order_id, item_id]

Using a properly defined primary key helps Lightdash optimize queries and provide accurate results when working with joined tables. It’s especially important for preventing metric inflation in SQL joins where duplicate rows can lead to incorrect aggregations.

Default show underlying values

You can set a default show_underlying_values configuration at the model level that applies to all metrics in that model. This is useful when you want consistent underlying data visibility across all metrics without repeating the same configuration for each metric. Individual metrics can override the model-level default by specifying their own show_underlying_values.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: payments
    meta:
      default_show_underlying_values:
        - orders.customer_id
        - unique_payment_count
      metrics:
        total_revenue:
          type: sum
          sql: ${TABLE}.revenue
          # Uses the model default: orders.customer_id, unique_payment_count
        average_revenue:
          type: average
          sql: ${TABLE}.revenue
          show_underlying_values:
            - custom_field  # Overrides the model default

models:
  - name: payments
    config:
      meta:
        default_show_underlying_values:
          - orders.customer_id
          - unique_payment_count
        metrics:
          total_revenue:
            type: sum
            sql: ${TABLE}.revenue
            # Uses the model default: orders.customer_id, unique_payment_count
          average_revenue:
            type: average
            sql: ${TABLE}.revenue
            show_underlying_values:
              - custom_field  # Overrides the model default

type: model
name: payments

default_show_underlying_values:
  - orders.customer_id
  - unique_payment_count

metrics:
  total_revenue:
    type: sum
    sql: ${TABLE}.revenue
    # Uses the model default: orders.customer_id, unique_payment_count
  average_revenue:
    type: average
    sql: ${TABLE}.revenue
    show_underlying_values:
      - custom_field  # Overrides the model default

The list of fields must be dimension names (or metrics if you’d like to include them) from the base table or from any joined tables. To reference a field from a joined table, prefix the field name with the joined table name, like this: my_joined_table_name.my_dimension. The order that the fields are listed in default_show_underlying_values is the order that they’ll appear in on the “View underlying data” table.

Default filters

Use default_filters to define filters on Dimensions that will be applied when no other user-defined filter on those Dimensions exists. Default filters will apply to tables on load and can be populated with a pre-determined value. Use them to suggest to users the kind of filters they might want to consider, or provide a default filtered view of a table that can be changed if needed. An optional required flag can be added, and if this flag is set to required: true, the filter for this field cannot be removed from the UI but users can still modify the operator and value to adjust how this filter is applied to the data. This ensures the filter is always applied in some form, while giving users flexibility to change the date range or other values. This can be particularly useful if you have a large table and want to force users to filter on a partitioned date. In contrast, when required: false (or omitted), the filter is pre-populated but fully editable — users can change the field, operator, value, or remove it entirely. Below you can see there is a default filter with the optional required flag, that will have show the last 14 days of data by default.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders
    meta:
      default_filters:
        - date: 'inThePast 14 days'
		  required: true
    columns:
      - name: date
        description: 'Order date'
        meta:
          dimension:
            type: date

models:
  - name: orders
    config:
      meta:
        default_filters:
          - date: 'inThePast 14 days'
		  required: true
    columns:
      - name: date
        description: 'Order date'
        config:
          meta:
            dimension:
              type: date

type: model
name: orders

default_filters:
  - date: 'inThePast 14 days'
    required: true

dimensions:
  - name: date
    description: 'Order date'
    type: date

Note: Required filters are not a security feature. For secure data access control, use user attributes.

If you have many filters in your list, they will be joined using `AND`

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

  - name: orders
    meta:
      default_filters:
        - date: 'inThePast 14 days'
        - status: "completed"
    columns:
      - name: date
        description: 'Order date'
        meta:
          dimension:
            type: date
      - name: status
        description: 'Order status - completed, pending, cancelled'
        meta:
          dimension:
            type: string

  - name: orders
    config:
      meta:
        default_filters:
          - date: 'inThePast 14 days'
          - status: "completed"
    columns:
      - name: date
        description: 'Order date'
        config:
          meta:
            dimension:
              type: date
      - name: status
        description: 'Order status - completed, pending, cancelled'
        config:
          meta:
            dimension:
              type: string

type: model
name: orders

default_filters:
  - date: 'inThePast 14 days'
  - status: "completed"

dimensions:
  - name: date
    description: 'Order date'
    type: date
  - name: status
    description: 'Order status - completed, pending, cancelled'
    type: string

In the example above, the orders table will have a default filter of date in the past 14 days and status completed. Both can be removed by the user, as the required flag is not present. Note that we do also support a legacy structure for defining required filters, see below:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders
    meta:
      required_filters:
        - date: 'inThePast 14 days'
    columns:
      - name: date
        description: 'Order date'
        meta:
          dimension:
            type: date

models:
  - name: orders
    config:
      meta:
        required_filters:
          - date: 'inThePast 14 days'
    columns:
      - name: date
        description: 'Order date'
        config:
          meta:
            dimension:
              type: date

type: model
name: orders

required_filters:
  - date: 'inThePast 14 days'

dimensions:
  - name: date
    description: 'Order date'
    type: date

Available filter types

Operator	Description	Example (as code)	Supported Types
is	Value is equal to	`user_name: "katie"`	String, Number, Date
is not	Value is not equal to	`user_name: "!katie"`	String, Number, Date
between	Value is between two values (inclusive)	`age: "between 18 and 65"`	Number, Date
		`order_date: "between 2024-01-01 and 2024-12-31"`	Number, Date
contains	String contains value	`user_name: "%katie%"`	String
does not contain	String does not contain value	`user_name: "!%katie%"`	String
starts with	String starts with value	`user_name: "katie%"`	String
ends with	String ends with value	`user_name: "%katie"`	String
is greater than	Number is greater than value	`num_orders: "> 4"`	Number
is greater than or equal to	Number is greater than or equal to value	`num_orders: ">= 4"`	Number
is less than	Number is less than value	`num_orders: "< 4"`	Number
is less than or equal to	Number is less than or equal to value	`num_orders: "<= 4"`	Number
in the past	Date is before x (days / months / years)	`date: "inThePast 14 days"`	Date
in the next	Date is after x (days / months / years)	`date: "inTheNext 30 days"`	Date
is null	Value is NULL	`status: "null"`	All
is not null	Value is not NULL	`status: "!null"`	All
is empty	Value is empty string	`notes: "empty"`	String
is not empty	Value is not empty string	`notes: "!empty"`	String
is [boolean]	Boolean value is true	`is_complete: "true"`	Boolean
is not [boolean]	Boolean value is false or null	`is_complete: "!true"`	Boolean
in list	Value is in comma-separated list	`status: "active,pending,approved"`	String, Number

Filter examples

Between operator (numeric):

default_filters:
  - age: 'between 18 and 65'
  - price: 'between 0.5 and 99.9'
  - temperature: 'between -10 and 10'

Between operator (dates):

default_filters:
  - order_date: 'between 2024-01-01 and 2024-12-31'
  - created_at: 'between 2024-01-01T00:00:00 and 2024-12-31T23:59:59'

Multiple values (comma-separated list):

default_filters:
  - status: 'active,pending,approved'
  - country: 'US,CA,UK'

Pattern matching:

default_filters:
  - product_name: '%shirt%'        # Contains "shirt"
  - email: 'admin%'                # Starts with "admin"
  - domain: '%@gmail.com'          # Ends with "@gmail.com"
  - product_name: '!%test%'        # Does not contain "test"

Date intervals:

default_filters:
  - created_date: 'inThePast 7 days'
  - expiry_date: 'inTheNext 30 days'
  - signup_date: 'inThePast 1 years'

Available intervals: milliseconds, seconds, minutes, hours, days, weeks, months, years Complex example with multiple filters:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders
    meta:
      default_filters:
        - status: 'completed,shipped'
        - order_date: 'between 2024-01-01 and 2024-12-31'
        - revenue: '>= 100'
        - country: '!test'
        - customer_email: '!%@test.com%'

models:
  - name: orders
    config:
      meta:
        default_filters:
          - status: 'completed,shipped'
          - order_date: 'between 2024-01-01 and 2024-12-31'
          - revenue: '>= 100'
          - country: '!test'
          - customer_email: '!%@test.com%'

type: model
name: orders

default_filters:
  - status: 'completed,shipped'
  - order_date: 'between 2024-01-01 and 2024-12-31'
  - revenue: '>= 100'
  - country: '!test'
  - customer_email: '!%@test.com%'

Filter notes

Case insensitivity: Keywords like between, AND, null, empty are case-insensitive
Quotes: Use quotes for strings with spaces or special characters
Escaping: Use ^ to escape special characters (%, _, ,, !, ^)
AND logic: Multiple filters are automatically joined with AND
Priority: Default filters only apply when no user-specified filter exists for that dimension

Parameters configuration

The parameters section allows you to define model-level parameters that can be referenced in various parts of your model’s SQL properties. These parameters are scoped to the specific model where they’re defined.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders
    meta:
      parameters:
        region:
          label: "Region"
          description: "Filter data by region"
          options:
            - "EMEA"
            - "AMER"
            - "APAC"
          default: ["EMEA", "AMER"]
          multiple: true
        min_order_value:
          label: "Minimum Order Value"
          description: "Filter for minimum order value"
          type: "number"
          options:
            - 100
            - 500
            - 1000
          default: 500
        department:
          label: "Department"
          description: "Filter data by department"
          options_from_dimension:
            model: "employees"
            dimension: "department"

models:
  - name: orders
    config:
      meta:
        parameters:
          region:
            label: "Region"
            description: "Filter data by region"
            options:
              - "EMEA"
              - "AMER"
              - "APAC"
            default: ["EMEA", "AMER"]
            multiple: true
          min_order_value:
            label: "Minimum Order Value"
            description: "Filter for minimum order value"
            type: "number"
            options:
              - 100
              - 500
              - 1000
            default: 500
          department:
            label: "Department"
            description: "Filter data by department"
            options_from_dimension:
              model: "employees"
              dimension: "department"

type: model
name: orders

parameters:
  region:
    label: "Region"
    description: "Filter data by region"
    options:
      - "EMEA"
      - "AMER"
      - "APAC"
    default: ["EMEA", "AMER"]
    multiple: true
  min_order_value:
    label: "Minimum Order Value"
    description: "Filter for minimum order value"
    type: "number"
    options:
      - 100
      - 500
      - 1000
    default: 500
  department:
    label: "Department"
    description: "Filter data by department"
    options_from_dimension:
      model: "employees"
      dimension: "department"

Each parameter is defined as a key-value pair where the key is the parameter name (must be alphanumeric with underscores or hyphens) and the value is an object with the following properties:

Property	Required	Value	Description
`label`	Yes	string	A user-friendly label for the parameter as it will be displayed in the UI.
`description`	No	string	A description of the parameter.
`type`	No	”string” or “number”	The type of the parameter. Defaults to “string” if not specified.
`options`	No	Array of strings or numbers	A list of possible values for the parameter.
`default`	No	string, number, or Array of strings/numbers	The default value(s) for the parameter.
`multiple`	No	boolean	Whether the parameter input will be a multi-select.
`allow_custom_values`	No	boolean	Whether users can input custom values beyond predefined options.
`options_from_dimension`	No	Object	Get parameter options from a dimension in a model. Requires `model` and `dimension` arguments (see below).

If using options_from_dimension, the object requires the following properties:

Property	Required	Value	Description
`model`	Yes	string	The model containing the dimension.
`dimension`	Yes	string	The dimension to get options from.

Using model-level parameters

Model-level parameters are referenced with the model name included in the syntax: ${lightdash.parameters.model_name.parameter_name} or the shorter alias ${ld.parameters.model_name.parameter_name}. For example, to reference a parameter named region from the current model:

${lightdash.parameters.orders.region}

Or using the shorter alias:

${ld.parameters.orders.region}

Using parameters from joined tables

You can also reference model-level parameters from joined tables. This is particularly useful when you want to use parameters defined in one model while working in another:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders
    meta:
      joins:
        - join: customers
          sql_on: |
            ${orders.customer_id} = ${customers.customer_id}
            AND ${customers.status} = ${ld.parameters.customers.customer_status}

models:
  - name: orders
    config:
      meta:
        joins:
          - join: customers
            sql_on: |
              ${orders.customer_id} = ${customers.customer_id}
              AND ${customers.status} = ${ld.parameters.customers.customer_status}

type: model
name: orders

joins:
  - join: customers
    sql_on: |
      ${orders.customer_id} = ${customers.customer_id}
      AND ${customers.status} = ${ld.parameters.customers.customer_status}

In this example, the join condition references a model-level parameter customer_status that is defined in the customers model, even though we’re configuring the orders model. See the Parameters guide for more examples and information on how to use parameters.

Explores

You can define multiple table explores from a single table using the explores config. This will allow you to list the same dbt model multiple times in the list of Tables in Lightdash. You can use it to show different versions of a table, join different tables to the base table, customize table visibility, etc. Below is an advanced example of using Explores. This will result in three total tables using the deals model at the base.

Deals will not have any joins or limitations
Deals w/Accounts will join to the accounts table and show all Accounts fields, but only people with the is_exec user attribute can see it
Deals w/Accounts (no Names) will join to the accounts table and only show Industry and Segment dimensions, it has no access restrictions

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
- name: deals
  meta:
    primary_key: deal_id
    explores:
      deals_accounts:
        required_attributes:
          is_exec: "true"
        label: 'Deals w/Accounts'
        description: The deals table with the Accounts table details included
        joins:
        - join: accounts
          relationship: many-to-one
          sql_on: ${deals.account_id} = ${accounts.account_id}
      deals_accounts_no_names:
        label: 'Deals w/Accounts (no Names)'
        description: The deals table with the Accounts table details included
        joins:
        - join: accounts
          relationship: many-to-one
          sql_on: ${deals.account_id} = ${accounts.account_id}
          fields: [industry, segment, unique_accounts, unique_smb_accounts, unique_midmarket_accounts, unique_enterprise_accounts]

models:
- name: deals
  config:
    meta:
      primary_key: deal_id
      explores:
        deals_accounts:
          required_attributes:
            is_exec: "true"
          label: 'Deals w/Accounts'
          description: The deals table with the Accounts table details included
          joins:
          - join: accounts
            relationship: many-to-one
            sql_on: ${deals.account_id} = ${accounts.account_id}
        deals_accounts_no_names:
          label: 'Deals w/Accounts (no Names)'
          description: The deals table with the Accounts table details included
          joins:
          - join: accounts
            relationship: many-to-one
            sql_on: ${deals.account_id} = ${accounts.account_id}
            fields: [industry, segment, unique_accounts, unique_smb_accounts, unique_midmarket_accounts, unique_enterprise_accounts]

type: model
name: deals

primary_key: deal_id
explores:
  deals_accounts:
    required_attributes:
      is_exec: "true"
    label: 'Deals w/Accounts'
    description: The deals table with the Accounts table details included
    joins:
    - join: accounts
      relationship: many-to-one
      sql_on: ${deals.account_id} = ${accounts.account_id}
  deals_accounts_no_names:
    label: 'Deals w/Accounts (no Names)'
    description: The deals table with the Accounts table details included
    joins:
    - join: accounts
      relationship: many-to-one
      sql_on: ${deals.account_id} = ${accounts.account_id}
      fields: [industry, segment, unique_accounts, unique_smb_accounts, unique_midmarket_accounts, unique_enterprise_accounts]

All the table configuration options can be used under the explores tag. Read this guide to learn more about explores

Sets

Sometimes you may find that you’re redeclaring the same set of fields for things like joins and show_underlying_values. In this case, you can define a set. A set allows you to associate those fields to a single value. That reference can then be used any place you would normally define fields.

# Define a set
sets:
  my_set:
    fields:
      - user_id
      - user_name
      - created_at

# Referencing the set
fields: [my_set*]

# Lightdash resolves to
fields: [user_id, user_name, created_at]

Expand

The expand operator (ex. my_set*) tells Lightdash to look up the set being referenced and resolve it to the associated collection of fields. When Lightdash compiles your model, it will replace set reference names with the actual fields.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders_model
    meta:
      sets:
        my_user_fields:
          fields:
            - user_id
            - user_name
    columns:
      - name: revenue
        meta:
          metrics:
            sum_revenue:
              type: sum
              show_underlying_values:
                - my_user_fields* # Reference to the set we defined

models:
  - name: orders_model
    config:
      meta:
        sets:
          my_user_fields:
            fields:
              - user_id
              - user_name
    columns:
      - name: revenue
        config:
          meta:
            metrics:
              sum_revenue:
                type: sum
                show_underlying_values:
                  - my_user_fields* # Reference to the set we defined

type: model
name: orders_model

sets:
  my_user_fields:
    fields:
      - user_id
      - user_name

dimensions:
  - name: revenue

metrics:
  sum_revenue:
    type: sum
    sql: ${TABLE}.revenue
    show_underlying_values:
      - my_user_fields* # Reference to the set we defined

Exclusions

In the instance where you want to leverage some, but not all fields in a set, you can use the excludes operator (ex. -field_name). The exclusion needs to be used in conjunction with an expand operator. This tells Lightdash to expand a given set while omitting any field name using the exclusion operator.

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: orders_model
    meta:
      sets:
        my_user_fields:
          fields:
            - user_id
            - user_name
    columns:
      - name: revenue
        meta:
          metrics:
            sum_revenue:
              type: sum
              show_underlying_values:
                - my_user_fields*
                - -user_id # Expand `my_user_fields` and exclude `user_id`

models:
  - name: orders_model
    config:
      meta:
        sets:
          my_user_fields:
            fields:
              - user_id
              - user_name
    columns:
      - name: revenue
        config:
          meta:
            metrics:
              sum_revenue:
                type: sum
                show_underlying_values:
                  - my_user_fields*
                  - -user_id # Expand `my_user_fields` and exclude `user_id`

type: model
name: orders_model

sets:
  my_user_fields:
    fields:
      - user_id
      - user_name

dimensions:
  - name: revenue

metrics:
  sum_revenue:
    type: sum
    sql: ${TABLE}.revenue
    show_underlying_values:
      - my_user_fields*
      - -user_id # Expand `my_user_fields` and exclude `user_id`

Joins

Within a model, you may join with other model tables. The sets you define can reference those joined fields as well! Use dot notation to reference a joined table:

dbt v1.9 and earlier
dbt v1.10+ and Fusion
Lightdash YAML

models:
  - name: purchases
    columns: <...>
    meta:
      sets:
  	    revenue_fields:
    	  fields:
            - purchase_date
            - purchase_amount
            - user.user_name # Referencing the joined table
      joins:
        - join: user
          sql_on: ${users.user_id} = ${purchases.user_id}
          relationship: one-to-many
      metrics:
        revenue:
          type: sum
          show_underlying_values:
            - revenue_fields*

models:
  - name: purchases
    columns: <...>
    config:
      meta:
        sets:
    	    revenue_fields:
      	  fields:
              - purchase_date
              - purchase_amount
              - user.user_name # Referencing the joined table
        joins:
          - join: user
            sql_on: ${users.user_id} = ${purchases.user_id}
            relationship: one-to-many
        metrics:
          revenue:
            type: sum
            show_underlying_values:
              - revenue_fields*

type: model
name: purchases

sets:
  revenue_fields:
    fields:
      - purchase_date
      - purchase_amount
      - user.user_name # Referencing the joined table

joins:
  - join: user
    sql_on: ${users.user_id} = ${purchases.user_id}
    relationship: one-to-many

metrics:
  revenue:
    type: sum
    sql: ${TABLE}.revenue
    show_underlying_values:
      - revenue_fields*

Remember, in order to include fields from another table in your set, you need ensure that you have joined the table the set originates from before defining the set.

Introduction

Explore and analyze

Build your semantic layer

Workspace and user management

Integrations

Lightdash SDK

Embedding

Self-hosting and deployment

Contact

Adding Tables to your project

Table configuration

Table properties

Adding a new dbt model

Order fields by

Group label

SQL from

SQL filter (row-level security)

Row-level security using user attributes

`sql_filter` will only be applied when querying tables directly.

If you reference a dimension from a joined table in your `sql_filter`, the referenced table will always be joined in your queries.

Required attributes

Any attributes

Defining primary keys

Single column primary key

Complex primary key

Default show underlying values

Default filters

If you have many filters in your list, they will be joined using `AND`

Available filter types

Filter examples

Filter notes

Parameters configuration

Using model-level parameters

Using parameters from joined tables

Explores

Sets

Expand

Exclusions

Joins

Introduction

Explore and analyze

Build your semantic layer

Workspace and user management

Integrations

Lightdash SDK

Embedding

Self-hosting and deployment

Contact

​Adding Tables to your project

​Table configuration

​Table properties

​Adding a new dbt model

​Order fields by

​Group label

​SQL from

​SQL filter (row-level security)

Row-level security using user attributes

sql_filter will only be applied when querying tables directly.

If you reference a dimension from a joined table in your sql_filter, the referenced table will always be joined in your queries.

​Required attributes

​Any attributes

​Defining primary keys

​Single column primary key

​Complex primary key

​Default show underlying values

​Default filters

​If you have many filters in your list, they will be joined using AND

​Available filter types

​Filter examples

​Filter notes

​Parameters configuration

​Using model-level parameters

​Using parameters from joined tables

​Explores

​Sets

​Expand

​Exclusions

​Joins

Adding Tables to your project

Table configuration

Table properties

Adding a new dbt model

Order fields by

Group label

SQL from

SQL filter (row-level security)

`sql_filter` will only be applied when querying tables directly.

If you reference a dimension from a joined table in your `sql_filter`, the referenced table will always be joined in your queries.

Required attributes

Any attributes

Defining primary keys

Single column primary key

Complex primary key

Default show underlying values

Default filters

If you have many filters in your list, they will be joined using `AND`

Available filter types

Filter examples

Filter notes

Parameters configuration

Using model-level parameters

Using parameters from joined tables

Explores

Sets

Expand

Exclusions

Joins