> For the complete documentation index, see [llms.txt](https://docs.aporia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aporia.com/data-sources/overview.md).

# Overview

**Aporia monitors your models by connecting *****directly***** to your data.** If you don't store your predictions yet, see our guide on [Storing Your Predictions](/storing-your-predictions/overview.md).

Aporia currently supports the following data sources:

* Amazon S3
* Athena
* BigQuery
* Databricks
* Glue Data Catalog
* Google Cloud Storage
* PostgreSQL
* Redshift
* Snowflake
* Azure Blob Storage
* MSSQL

{% hint style="info" %}
If your storage or database are not shown here, please contact your Aporia account manager for further assistance.
{% endhint %}

### Configure Data Source

Connecting to a data source begins with configuring its connection details. For example, to connect to a Postgres database, we can create a data source using the following details:

```python
url="jdbc:postgresql://<POSTGRES_HOSTNAME>/<DBNAME>",
user="<DB_USER>",
password="<DB_PASSWORD>"
```

{% hint style="info" %}
Please refer to the documentation page of the relevant data source for a complete list of requirements and configuration options.
{% endhint %}

### Link Your Data

After creating a data source, we can create a model version and link our data. This process consist of two steps:

1. **Linking a dataset** - define a query to retrieve your data.
2. **Mapping model schema** - map your model's raw inputs, features, predictions, and actuals. The fields you can map are from the columns you retrieved.

The ground truth can be `NULL` until it actually has value, that's okay.

### You are good to go, it's time to get value! :tada:


---

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