https://github.com/metriql/metriql-metabase
Metriql Metabase integration
https://github.com/metriql/metriql-metabase
database dbt metadata metrics
Last synced: about 1 month ago
JSON representation
Metriql Metabase integration
- Host: GitHub
- URL: https://github.com/metriql/metriql-metabase
- Owner: metriql
- License: mit
- Created: 2021-09-16T22:54:59.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-10-04T15:28:46.000Z (almost 5 years ago)
- Last Synced: 2025-12-15T10:41:35.838Z (7 months ago)
- Topics: database, dbt, metadata, metrics
- Language: Python
- Homepage: https://metriql.com/integrations/bi-tools/metabase
- Size: 21.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Metriql Metabase Integration
Synchronize Metabase datasets from Metriql datasets. The idea is to leverage Metriql datasets in your Metabase workflow without any additional modeling in Metabase.
### Usage
The library is available in PyPI so you can install it via pip as follows:
```
pip install metriql-metabase
```
The library expects `stdin` for the Metriql metadata and interacts with Metabase via its API. Here is an example:
```
curl http://metriql-server.com/api/v0/metadata | metriql-metabase --metriql-url http://metriql-server.com --metabase-username USERNAME --metabase-password PASSWORD --metabase-database METABASE_DATABASE_NAME sync-database
```
You can use `--file` argument instead of reading the metadata from `stdin` as an alternative.
Available commands are `list-databases`, `sync-database`.
### FAQ
#### Do you support Metabase Cloud?
Yes!
#### How is this related to [dbt-metabase](https://github.com/gouline/dbt-metabase)?
While this metriql-metabase is heavily influenced by the [dbt-metabase](https://github.com/gouline/dbt-metabase) codebase,
it integrates Metabase with Metriql, not directly to dbt. While you need to maintain Metriql as a separate service, here are advantages of Metriql over dbt-metabase:
* You can define the metrics as native SQL
* You can leverage [Aggregates](https://metriql.com/introduction/aggregates) to speed up your queries
* Sync the datasets into [various data tools](https://metriql.com/integrations/bi-tools/index), not just Metabase
* Native [MQL](https://metriql.com/query/mql) experience when running ad-hoc queries on data.