Model synchronization from dbt to Metabase.

If dbt is your source of truth for database schemas and you use Metabase as your analytics tool, dbt-metabase can propagate table relationships, model and column descriptions and special types (e.g. currency, category, URL) to your Metabase data model.

Requires Python 3.6 or above.

You can install dbt-metabase from PyPI:

pip install dbt-metabase

Let's start by defining a short sample schema.yml as below.

models:
  - name: stg_users
    description: User records.
    columns:
      - name: id
        description: Primary key.
        tests:
          - not_null
          - unique
      - name: email
        description: User's email address.
      - name: group_id
        description: Foreign key to user group.
        tests:
          - not_null
          - relationships:
              to: ref('groups')
              field: id
  - name: stg_groups
    description: User groups.
    columns:
      - name: id
        description: Primary key.
        tests:
          - not_null
          - unique
      - name: name
        description: Group name.

That's already enough to propagate the primary keys, foreign keys and descriptions to Metabase by executing the below command.

dbt-metabase export \
    --dbt_path . \
    --mb_host metabase.example.com \
    --mb_user [email protected] \
    --mb_password Password123 \
    --database business \
    --schema public

Check your Metabase instance by going into Settings > Admin > Data Model, you will notice that ID in STG_USERS is now marked as "Entity Key" and GROUP_ID is marked as "Foreign Key" pointing to ID in STG_GROUPS.

Now that we have primary and foreign keys, let's tell Metabase that email column contains email addresses.

Change the email column as follows:

- name: email
  description: User's email address.
  meta:
    metabase.special_type: type/Email

Once you run dbt-metabase export again, you will notice that EMAIL is now marked as "Email".

Here is the list of special types currently accepted by Metabase:

• type/PK
• type/FK
• type/AvatarURL
• type/Category
• type/City
• type/Country
• type/Currency
• type/Description
• type/Email
• type/Enum
• type/ImageURL
• type/SerializedJSON
• type/Latitude
• type/Longitude
• type/Number
• type/State
• type/URL
• type/ZipCode
• type/Quantity
• type/Income
• type/Discount
• type/CreationTimestamp
• type/CreationTime
• type/CreationDate
• type/CancelationTimestamp
• type/CancelationTime
• type/CancelationDate
• type/DeletionTimestamp
• type/DeletionTime
• type/DeletionDate
• type/Product
• type/User
• type/Source
• type/Price
• type/JoinTimestamp
• type/JoinTime
• type/JoinDate
• type/Share
• type/Owner
• type/Company
• type/Subscription
• type/Score
• type/Title
• type/Comment
• type/Cost
• type/GrossMargin
• type/Birthdate

If you notice new ones, please submit a PR to update this readme.

In addition to special types, you can optionally specify visibility for each field. This affects whether or not they are displayed in the Metabase UI.

Here is how you would hide that same email:

- name: email
  description: User's email address.
  meta:
    metabase.special_type: type/Email
    metabase.visibility_type: sensitive

Here are the visibility types supported by Metabase:

• normal (default)
• details-only
• sensitive
• hidden (supported but not reflected in the UI)
• retired (supported but not reflected in the UI)

If you notice new ones, please submit a PR to update this readme.

By default, dbt-metabase will tell Metabase to synchronize database fields and wait for the data model to contain all the tables and columns in your dbt project.

You can control this behavior with two arguments:

• --sync - boolean to enable or disable pre-synchronization
• --sync_timeout - number of seconds to wait and re-check data model before giving up

As you have already seen, you can invoke dbt-metabase from the command line. But if you prefer to call it from your code, here's how to do it:

import dbtmetabase

dbtmetabase.export(dbt_path, mb_host, mb_user, mb_password, database, schema)

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