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"
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:
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
joins:
- join: events
sql_on: ${users.user_id} = ${events.user_id}
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
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:
models:
- name: users
meta:
order_fields_by: 'index'
columns:
- name: user_id
- name: user_name
- name: user_email
- 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.
models:
- name: sales
meta:
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:
models:
- name: sales
meta:
sql_filter: ${TABLE}.sales_region = 'EMEA'
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:
models:
- name: sales
meta:
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.
models:
- name: payments
meta:
required_attributes:
is_admin: "true"
Forbidden error.
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 show apply to tables on load and can be populated with a pre-determined value. User 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 - in which case the filter cannot be removed. This can be particulalry useful if you have a large table and want to force users to filter on a partitioned date.
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.
models:
- name: orders
meta:
default_filters:
- date: 'inThePast 14 days'
required: true
columns:
- name: date
description: 'Order date'
meta:
dimension:
type: date
A required filter’s field reference can’t be changed, but its operator (is, is not, etc.) and value can be changed when querying the table from the UI.
If you have many filters in your list, they will be joined using AND
- 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
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:
models:
- name: orders
meta:
required_filters:
- date: 'inThePast 14 days'
columns:
- name: date
description: 'Order date'
meta:
dimension:
type: date
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:
models:
- name: users
meta:
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:
models:
- name: order_items
meta:
primary_key: [order_id, item_id]
Available filter types
| Type | Example (in English) | Example (as code) |
|---|
| is | User name is equal to katie | user_name: “katie” |
| is not | User name is not equal to katie | user_name: “!katie” |
| contains | User name contains katie | user_name: “%katie%“ |
| does not contain | User name does not contain katie | user_name: ”!%katie%“ |
| starts with | User name starts with katie | user_name: “katie%“ |
| ends with | User name ends with katie | user_name: “%katie” |
| is greater than (number) | Number of orders is greater than 4 | num_orders: ”> 4” |
| in the past (date) (interval) | Date is before x (days / months / years) | date: “inThePast 14 months” |
| in the next (date) (interval) | Date is after x (days / months / years) | date: “inTheNext 14 days” |
| is greater than or equal to | Number of orders is greater than or equal to 4 | num_orders: ”>= 4” |
| is less than | Number of orders is less than 4 | num_orders: ”< 4” |
| is less than or equal to | Number of orders is less than or equal to 4 | num_orders: ”<= 4” |
| is null | Status is NULL | status: “null” |
| is not null | Status is not NULL | status: “!null” |
| is [boolean] | Is complete is true | is_complete: “true” |
| is not [boolean] | Is complete is false or null | is_complete: “!true” |
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.
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"
| 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}
${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:
models:
- name: orders
meta:
joins:
- join: customers
sql_on: |
${orders.customer_id} = ${customers.customer_id}
AND ${customers.status} = ${ld.parameters.customers.customer_status}
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
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]
explores tag.
Read this guide to learn more about explores 
