# Data Connectors

For data that changes frequently — sales transactions, user events, operational metrics — file uploads become tedious. Data connectors solve this by establishing a live link to your source systems.

Once connected, you can refresh data on demand or schedule automatic syncs. Your analyses stay current without manual intervention.

**Available connectors:**

* **Databases:** [PostgreSQL](/data-connectors/postgresql.md), [MySQL](/data-connectors/mysql.md), [MongoDB](/data-connectors/mongodb.md)
* **Data Warehouses:** [Snowflake](/data-connectors/snowflake.md) *(Beta)*
* **Cloud Storage:** [Amazon S3](/data-connectors/s3.md), [Azure Data Lake](/data-connectors/azure-data-lake.md)
* **APIs:** [REST API](/data-connectors/rest-api.md), [Google Analytics](/data-connectors/google-analytics.md)

**Coming soon:** BigQuery, Azure Synapse, Databricks

**To connect a data source:**

1. Navigate to **Data** → **Datasets** and click **+ Import Data Sources**
2. In the Import Manager, locate the connector type and click **Setup**
3. Enter connection credentials
4. Configure a sync schedule and save

<figure><img src="/files/Hv6OfYz7QpLEm4kmK7V8" alt="Import manager showing available connector types"><figcaption><p>The import manager. Select a connector type to begin the connection setup.</p></figcaption></figure>

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### Next steps

Once your data is connected:

1. Create a [Data View](/user-guide/data-views.md) to transform and combine your datasets
2. Tag columns with [Ontology Concepts](/user-guide/ontology-concepts.md) to link related data across sources
3. [Build a Digital Twin](/user-guide/creating-digital-twin.md) using your prepared Data View


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