dbt Integration

Overview

Hashboard can import models directly from dbt by specifying measures in your dbt yaml file. Hashboard will automatically sync dbt documentation and keep measures in sync with downstream dashboards (whether they are checked into version control or not). The Hashboard dbt integration can also be integrated in CI/CD workflows.

models:
  +meta:
    # hashboard-defaults settings will be merged into each Hashboard model
    hashboard-defaults:
      hbVersion: "1.0"
      # preventUpdatesFromUI defaults to true, but can be set to false
      preventUpdatesFromUI: false
      source:
        connectionName: bigquery-prod # the name of the conneciton in Hashboard

version: 2
 
models:
  #
  - name: order_attribution
    description: This model describes how each order is attributed to different marketing touchpoints.
    columns:
      - name: attribution_id
        description: Unique ID for the attribution.
      - name: order_id
        description: Order ID, matches the order_id in the sales model.
      - name: touchpoint_id
        description: ID of the marketing touchpoint which led to the order.
    meta:
      hashboard:
        cols:
          - id: row_count
            type: metric
            name: Order Attribution Records
            aggregate: row_count

# assuming you've already built your tables with dbt run
hb preview --dbt
 
# and later
hb deploy --dbt

Model specification

The hashboard-defaults connection configuration in the example above gets merged into the Hashboard configuration inside each dbt model. If you don't specify the defaults, you must add the complete connection information in the meta key of each dbt model you want imported to Hashboard. Only dbt models that specify a hashboard key will get built into Hashboard models.

version: 2
 
models:
  - name: a_model
    description: model docs here
    meta:
      hashboard:
        hbVersion: "1.0" # Hashboard dbt integration version
        source:
          connectionName: snowflake_production # which Hashboard data connection to use, can also be specified globally if you use hashboard-defaults
    columns:
      - name: a_str
        description: some column documentation
      # ... your dbt columns

Some notes on how models get built from dbt:

• Hashboard will read the dbt manifest file and find all models with a hashboard key specified within the meta key.
• The name of dbt model is imported into Hashboard as the model name (this can be overriden).
• Columns specified explicitly in the dbt model will become attributes in Hashboard.
• Descriptions on the model will get imported as model documentation.
• You can specify additional attribute configuration in a meta key for a column.

How data model source is determined:

• source has the same options as the corresponding field of Hashboard data models.
• Unless otherwise specified, Hashboard automatically infers defaults for the source physicalName and schema. If no connectionName or connectionId is specified, Hashboard will try to use a connection whose name is the same as the dbt target database (e.g. "bigquery" or "snowflake").

You may then use dbt-core to produce a Manifest JSON file (opens in a new tab), which should be included with any Hashboard deploy (or preview):

hb preview --dbt-manifest=./target/manifest.json

Hashboard will include each dbt column as an attribute in the corresponding Hashboard model. You can also add additional fields to the dbt columns to take advantage of Hashboard's more advanced modeling features:

# ... as above
columns:
  - name: my_dbt_attribute
    documentation: this is a dbt attribute
    tests:
      - unique
      - not_null
    # add additional configuration to this column
    meta:
      hashboard:
        primaryKey: true

To add Hashboard measures (or other attributes not present in the dbt model) to the imported model, you can add a cols field to the dbt model's meta.hashboard configuration:

models:
  - # ... dbt schema configuration, as above
    meta:
      hashboard:
        cols:
          - id: daily_active_users
            type: metric
            name: Daily Active Users
            sql: COUNT(DISTINCT customer_id) / COUNT(DISTINCT create_at)

Hashboard configuration in your dbt schema file has access to the same templating and macros (opens in a new tab) as your dbt configuration. You could, for example, configure the Hashboard data connection based on your dbt target:

models:
  - # ... dbt model configuration
    meta:
      hashboard:
        hbVersion: "1.0"
        name: "customers"
        source:
          # configure the source based on the dbt target
          connectionName: "{{ target.database }}"

Imported dbt models are exclusively controlled by code and cannot be saved from the web UI, though you are still free to prototype and experiment with dbt models in the Hashboard model editor.

Column descriptions will be automatically synced from dbt to the corresponding Hashboard model during deploys.

Global configuration

If all models use the same Hashboard source, you can configure a default source in the dbt_project.yml file:

models:
  the_models:
    +meta: # will be applied to every model in models/the_models
      hashboard-defaults:
        source:
          connectionName: # which Hashboard data connection to use

To import all dbt models in a folder as Hashboard models, simply add

models:
  the_models:
    +meta:
      hashboard-defaults:
        hbVersion: "1.0"
        source:
          connectionName: # which Hashboard data connection to use
          # schema is always optional because Hashboard will infer it
          schema: "{{ target.schema }}" # can use jinja substitution here

This configuration will add the meta.hashboard object to every model in the_models, so they will all be imported into Hashboard. You can still add additional metadata for the specific models (such as adding additional metrics).