Skip to content

gouline/dbt-metabase

master
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

dbt-metabase

GitHub Actions PyPI Downloads License: MIT Code style: black

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 semantic types (e.g. currency, category, URL) to your Metabase data model.

Requirements

Requires Python 3.6 or above.

Main features

The main features provided by dbt-metabase are:

  • Parsing your dbt project (either through the manifest.json or directly through the YAML files)
  • Triggering a Metabase schema sync before propagating the metadata
  • Propagating table descriptions to Metabase
  • Propagating columns description to Metabase
  • Propagating columns semantic types and visibility types to Metabase through the use of dbt meta fields
  • Propagating table relationships represented as dbt relationships column tests
  • Extracting dbt model exposures from Metabase and generating YAML files to be included and revisioned with your dbt deployment

Usage

You can install dbt-metabase from PyPI:

pip install dbt-metabase

Sections below demonstrate basic usage examples, for all CLI options:

dbt-metabase --help

When invoking programmatically, click through to implementation and refer to header comments.

Basic Example

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 models \
    --dbt_path . \
    --dbt_database business \
    --metabase_host metabase.example.com \
    --metabase_user user@example.com \
    --metabase_password Password123 \
    --metabase_database business \
    --dbt_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.

Exposure Extraction

dbt-metabase also allows us to extract exposures from Metabase. The invocation is almost identical to our models function with the addition of output name and location args. dbt exposures let us understand how our dbt models are exposed in BI which closes the loop between ELT, modelling, and consumption.

dbt-metabase exposures \
    --dbt_manifest_path ./target/manifest.json \
    --dbt_database business \
    --metabase_host metabase.example.com \
    --metabase_user user@example.com \
    --metabase_password Password123 \
    --metabase_database business \
    --output_path ./models/ \
    --output_name metabase_exposures

Once execution completes, a look at the output metabase_exposures.yml will reveal all metabase exposures documented with the documentation, descriptions, creator emails & names, links to exposures, and even native SQL propagated over from Metabase.

exposures:
  - name: Number_of_orders_over_time
    description: '
      ### Visualization: Line

      A line chart depicting how order volume changes over time

      #### Metadata

      Metabase Id: __8__

      Created On: __2021-07-21T08:01:38.016244Z__'
    type: analysis
    url: http://your.metabase.com/card/8
    maturity: medium
    owner:
      name: Indiana Jones
      email: user@example.com
    depends_on:
      - ref('orders')

Questions which are native queries will have the SQL propagated to a code block in the documentation's description for full visibility. This YAML, like the rest of your dbt project can be committed to source control to understand how exposures change over time. In a production environment, one can trigger dbt docs generate after dbt-metabase exposures (or alternatively run the exposure extraction job on a cadence every X days) in order to keep a dbt docs site fully synchronized with BI. This makes dbt docs a useful utility for introspecting the data model from source -> consumption with zero extra/repeated human input.

Reading your dbt project

There are two approaches provided by this library to read your dbt project:

1. Artifacts

You can instruct dbt-metabase to read your manifest.json, a dbt artifact containing the full representation of your dbt project's resources. If your dbt project uses multiple schemas, multiple databases or model aliases, you must use this approach.

Note that you you have to run dbt compile --target prod or any of the other dbt commands listed in the dbt documentation above to get a fresh copy of your manifest.json. Remember to run it against your production target.

When using the dbt-metabase CLI, you must provide a --dbt_manifest_path argument pointing to your manifest.json file (usually in the target/ folder of your dbt project).

2. Direct parsing

Alternatively, you can provide the path to your dbt project root folder using the argument --dbt_path. dbt-metabase will then look for all .yml files and parse your documentation and tests directly from there. It does not support dbt projects with custom schemas.

Semantic Types

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.semantic_type: type/Email

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

Here are common semantic types (formerly known as special types) accepted by Metabase:

  • type/PK
  • type/FK
  • type/Number
  • type/Currency
  • type/Category
  • type/Title
  • type/Description
  • type/City
  • type/State
  • type/ZipCode
  • type/Country
  • type/Latitude
  • type/Longitude
  • type/Email
  • type/URL
  • type/ImageURL
  • type/SerializedJSON
  • type/CreationTimestamp

See documentation for a more complete list.

Foreign Keys

Built-in relationship tests are the recommended way of defining foreign keys, however you can alternatively use fk_target_table and fk_target_field meta fields (semantic_type is optional and will be inferred). If both are set for a column, meta fields take precedence.

- name: country_id
  description: FK to User's country in the dim_countries table.
  meta:
    metabase.semantic_type: type/FK
    metabase.fk_target_table: analytics_dims.dim_countries
    metabase.fk_target_field: id

You can provide fk_target_table in the format schema_name.table_name or just table_name to use the current schema. If your model has an alias, provide that alias (rather than the original name).

Visibility Types

In addition to semantic types, you can optionally specify visibility for each table and 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.semantic_type: type/Email
    metabase.visibility_type: sensitive

Here are the field visibility types supported by Metabase:

  • normal (default)
  • details-only
  • sensitive

Tables only support the following:

  • No value for visible (default)
  • hidden
  • technical
  • cruft

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

Model Extra Fields

In addition to the model description, Metabase accepts two extra information fields. Those optional fields are called caveats and points_of_interest and can be defined under the meta tag of the model.

This is how you can specify them in the stg_users example:

- name: stg_users
  description: User records.
  meta:
    metabase.points_of_interest: Relevant records.
    metabase.caveats: Sensitive information about users.

Database Sync

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:

  • --metabase_sync_skip - boolean to optionally disable pre-synchronization
  • --metabase_sync_timeout - number of seconds to wait and re-check data model before giving up

Configuration

dbt-metabase config

Using the above command, you can enter an interactive configuration session where you can cache default selections for arguments. This creates a config.yml in ~/.dbt-metabase. This is particularly useful for arguments which are repeated on every invocation like metabase_user, metabase_host, metabase_password, dbt_manifest_path, etc.

In addition, there are a few injected env vars that make deploying dbt-metabase in a CI/CD environment simpler without exposing secrets. Listed below are acceptable env vars which correspond to their CLI flags:

  • DBT_DATABASE
  • DBT_PATH
  • DBT_MANIFEST_PATH
  • MB_USER
  • MB_PASSWORD
  • MB_HOST
  • MB_DATABASE

If any one of the above is present in the environment, the corresponding CLI flag is not needed unless overriding the environment value. In the absence of a CLI flag, dbt-metabase will first look to the environment for any env vars to inject, then we will look to the config.yml for cached defaults.

A config.yml can be created or updated manually as well if needed. The only requirement is that it must be located in ~/.dbt-metabase. The layout is as follows:

config:
    dbt_database: reporting
    dbt_manifest_path: /home/user/dbt/target/manifest.json
    metabase_database: Reporting
    metabase_host: reporting.metabase.io
    metabase_user: user@source.co
    metabase_password: ...
    metabase_use_http: false
    metabase_sync: true
    metabase_sync_timeout: null
    dbt_schema_excludes:
      - development
      - testing
    dbt_excludes:
      - test_monday_io_site_diff

Programmatic Invocation

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:

from dbtmetabase.models.interface import MetabaseInterface, DbtInterface

# Instantiate dbt interface
dbt = DbtInterface(
    path=dbt_path,
    manifest_path=dbt_manifest_path,
    database=dbt_database,
    schema=dbt_schema,
    schema_excludes=dbt_schema_excludes,
    includes=dbt_includes,
    excludes=dbt_excludes,
)

# Load models
dbt_models, aliases = dbt.read_models(
    include_tags=dbt_include_tags,
    docs_url=dbt_docs_url,
)

# Instantiate Metabase interface
metabase = MetabaseInterface(
    host=metabase_host,
    user=metabase_user,
    password=metabase_password,
    use_http=metabase_use_http,
    verify=metabase_verify,
    database=metabase_database,
    sync=metabase_sync,
    sync_timeout=metabase_sync_timeout,
)

# Propagate models to Metabase
metabase.client.export_models(
    database=metabase.database,
    models=dbt_models,
    aliases=aliases,
)

# Parse exposures from Metabase into dbt schema yml
metabase.client.extract_exposures(
    models=dbt_models,
    output_path=output_path,
    output_name=output_name,
    include_personal_collections=include_personal_collections,
    collection_excludes=collection_excludes,
)

Code of Conduct

All contributors are expected to follow the PyPA Code of Conduct.