Dashboard Filter

Creating a Dashboard Filter in DataBrain

To add a dashboard filter, click on the ”+” sign located below the dashboard’s name. Start by selecting the datatype for the filter, which will influence the available filter option types—auto, manual, or custom. Note that the option type may vary based on the selected datatype.

Customizations Based on Datatype:

Filter Variant: Choose between single select, multi-select, or search options for how users can interact with the filter.
Select Default Value: Automatically select a default value or allow users to choose one. This setting depends on the datatype.
Scope to Client: Filter options will be tailored based on the client. This ensures that filter choices are relevant to a specific client’s data context.
Label Column Setup: Configure a label column that displays descriptive labels for the values being filtered. This enhances readability and user understanding of what each filter value represents.

Dependent Filters

Use dependent filters when the available options in one filter should be constrained by the selection in another filter (for example, Store City depending on Store State).

Columns from Same Table

Create two filters (Store State and Store City) on the same dataset, and configure Store City to depend on Store State. Store State

Dashboard Filters configuration for Store State filter using the main.demo_sales dataset

Store City depends on Store State

Dashboard Filters configuration for Store City filter with dependency on Store State

Columns from Different Tables

When the top-level and dependent filters use columns from different tables, configure the dependency using a custom SQL query that joins the tables. Step 1: Configure the Top-Level Filter Set up the first-level filter as you normally would.

Dashboard Filters configuration for Level 1 filter on the coffees dataset

Step 2: Configure the Dependent (Second-Level) Filter For the second-level filter (which needs to depend on the first filter), select Custom as the filter type and write the SQL in the following format:

SELECT
  Concat_ws(
    ' ',
    "main_customers"."first_name",
    "main_customers"."last_name"
  ) AS "name",    /* Label for this filter */
  "main_customers"."customer_id" AS "customer_id", /* Column for this filter */
  "main_coffees"."coffee_id" AS "coffee_id"       /* Connecting column between Level 1 and Level 2 */

FROM
  "main"."coffees" AS "main_coffees"
  INNER JOIN "main"."orders" AS "main_orders"
    ON "main_coffees"."coffee_id" = "main_orders"."coffee_id"
  INNER JOIN "main"."customers" AS "main_customers"
    ON "main_orders"."customer_id" = "main_customers"."customer_id"

Make sure the query includes the connecting column between Level 1 and Level 2.

The necessary joins are added to establish the relationship between the two filter tables. Step 3: Configure Dependency In the filter setup, you will see that the column from Level 1 is part of the above query.

In Advanced Options, under Depend On, type the connecting column from Filter 1 to ensure linkage.

This ensures that Filter 2 is correctly dependent on Filter 1.

Dashboard Filters configuration showing Depend On section linking the second-level filter to the first-level filter via the connecting column

Applying the Filter:

Once the filter settings are configured, decide where to apply these filters within the dashboard:

Direct Apply: Immediately apply the filter to the dashboard elements.
Variable for Custom SQL: Obtain a variable that can be used in your custom SQL queries, allowing for more tailored data interactions.

For a detailed guide on Variable Apply On, kindly refer the below link: