If your dbt project uses multiple schemas, The full representation of your dbt project's resources. You can instruct dbt-metabase to read your manifest.json, a dbt artifact containing There are two approaches provided by this library to read your dbt project: 1. Useful utility for introspecting the data model from source -> consumption with zero extra/repeated human input. On a cadence every X days) in order to keep a dbt docs site fully synchronized with BI. In a production environment, one can triggerĭbt docs generate after dbt-metabase exposures (or alternatively run the exposure extraction job This YAML, like the rest of your dbt project can be committed to sourceĬontrol to understand how exposures change over time. Questions which are native queries will have the SQL propagated to a code block in the documentation'sĭescription for full visibility. 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: _T08:01:38.016244Z_' type : analysis url : maturity : medium owner : name : Indiana Jones email : depends_on : - ref('orders') Reveal all metabase exposures documented with the documentation, descriptions, creatorĮmails & names, links to exposures, and even native SQL propagated over from Metabase. Once execution completes, a look at the output metabase_exposures.yml will models/ \ -output_name metabase_exposures target/manifest.json \ -dbt_database business \ -metabase_host \ -metabase_user \ -metabase_password Password123 \ -metabase_database business \ -output_path. dbt-metabase exposures \ -dbt_manifest_path. How our dbt models are exposed in BI which closes the loop between ELT, modelling, and consumption. Our models function with the addition of output name and location args. Exposure Extractionĭbt-metabase also allows us to extract exposures from Metabase. GROUP_ID is marked as "Foreign Key" pointing to ID in STG_GROUPS. Will notice that ID in STG_USERS is now marked as "Entity Key" and \ -dbt_database business \ -metabase_host \ -metabase_user \ -metabase_password Password123 \ -metabase_database business \ -dbt_schema publicĬheck your Metabase instance by going into Settings > Admin > Data Model, you That's already enough to propagate the primary keys, foreign keys andĭescriptions to Metabase by executing the below command. tests : - not_null - unique - name : name description : Group name. columns : - name : id description : Primary key. tests : - not_null - relationships : to : ref('groups') field : id - name : stg_groups description : User groups. name : group_id description : Foreign key to user group. tests : - not_null - unique - name : email description : User's email address. models : - name : stg_users description : User records. Let's start by defining a short sample schema.yml as below. When invoking programmatically, click through to implementation and refer to header comments. Sections below demonstrate basic usage examples, for all CLI options: dbt-metabase -help You can install dbt-metabase from PyPI: pip install dbt-metabase Extracting dbt model exposures from Metabase and generating YAML files to be included and revisioned with your dbt deployment.Propagating table relationships represented as dbt relationships column tests.Propagating columns semantic types and visibility types to Metabase through the use of dbt meta fields.Propagating columns description to Metabase.Propagating table descriptions to Metabase.Triggering a Metabase schema sync before propagating the metadata.Parsing your dbt project (either through the manifest.json or directly through the YAML files).The main features provided by dbt-metabase are: Your analytics tool, dbt-metabase can propagate table relationships, model andĬolumn descriptions and semantic types (e.g. If dbt is your source of truth for database schemas and you use Metabase as Model synchronization from dbt to Metabase.
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