Data observability for analytics & data engineers
Monitor your data quality, operation and performance directly from your dbt project.
To learn more, refer to our [main repo »](https://github.com/elementary-data/elementary) | [Demo »](https://bit.ly/3IXKShW)
## Quick start
Add to your `packages.yml` according to your dbt version:
#### For dbt 1.2.0 and above:
```yml
packages:
- package: elementary-data/elementary
version: 0.5.3
## Docs: https://docs.elementary-data.com
```
#### For dbt >=1.0.0 <1.2.0:
```yml
packages:
- package: elementary-data/elementary
version: 0.5.3
## Docs: https://docs.elementary-data.com
## !! Important !! For dbt <1.2.0 only
## (Prevents dbt_utils versions exceptions)
- package: dbt-labs/dbt_utils
version: [">=0.8.0", "<0.9.0"]
```
After adding to `packages.yml` and running `dbt deps`, add to your ```dbt_project.yml```:
```yml
models:
## elementary models will be created in the schema '_elementary'
## for details, see docs: https://docs.elementary-data.com/
elementary:
+schema: 'elementary'
```
And run ```dbt run --select elementary```.
Check out the [full documentation](https://docs.elementary-data.com/) for generating the UI, alerts and adding anomaly detection tests.
## Run Results and dbt artifacts
The package automatically uploads the dbt artifacts and run results to tables in your data warehouse:
Run results tables:
- dbt_run_results
- model_run_results
- snapshot_run_results
- elementary_test_results (all dbt test results)
Metadata tables:
- dbt_models
- dbt_tests
- dbt_sources
- dbt_exposures
- dbt_metrics
- sbt_sbapshots
Here you can find [additional details about the tables](https://docs.elementary-data.com/guides/modules-overview/dbt-package).
## Data anomalies detection as dbt tests
Elementary dbt tests collect metrics and metadata over time, such as freshness, volume, schema changes, distribution, cardinality, etc.
Executed as any other dbt tests, the Elementary tests alert on anomalies and outliers.
**Elementary tests are configured and executed like native tests in your project!**
Example of Elementary test config in ```properties.yml```:
```yml
models:
- name: your_model_name
config:
elementary:
timestamp_column: updated_at
tests:
- elementary.table_anomalies
- elementary.all_columns_anomalies
```
## Data observability report
## Slack alerts
## How it works?
Elementary dbt package creates tables of metadata and test results in your data warehouse, as part of your dbt runs. The [CLI tool](https://github.com/elementary-data/elementary) reads the data from these tables, and is used to generate the UI and alerts.
## Data warehouse support
* [x] **Snowflake** 
* [x] **BigQuery** 
* [x] **Redshift** 
* [x] **Databricks SQL** 
## Community & Support
* [Slack](https://join.slack.com/t/elementary-community/shared_invite/zt-uehfrq2f-zXeVTtXrjYRbdE_V6xq4Rg) (Talk to us, support, etc.)
* [GitHub issues](https://github.com/elementary-data/elementary/issues) (Bug reports, feature requests)
## Contributions
Thank you :orange_heart: Whether it’s a bug fix, new feature, or additional documentation - we greatly appreciate contributions!
Check out the [contributions guide](https://docs.elementary-data.com/general/contributions) and [open issues](https://github.com/elementary-data/elementary/issues) in the main repo.