> ## Documentation Index
> Fetch the complete documentation index at: https://docs.woodwide.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Connections

> Connect warehouses and object storage, then import external data as Wood Wide datasets.

Connections let you pull data from external systems into Wood Wide without manually downloading and re-uploading files. Create and credential a connection in the [console](https://console.woodwide.ai/connections/new), then browse schemas or objects and import into a dataset for training and inference.

Supported providers include **BigQuery**, **Snowflake**, **PostgreSQL**, **Databricks**, **S3**, and **GCS**.

<CardGroup cols={2}>
  <Card title="Add a connection" icon="plus" href="https://console.woodwide.ai/connections/new">
    Create and test a connection in the console.
  </Card>

  <Card title="CLI import" icon="terminal" href="/cli#data-connections">
    Import from an existing connection with `wwai connections`.
  </Card>
</CardGroup>

***

## Console workflow

<Steps>
  <Step title="Create a connection">
    In the console, open **Connections** → **Add connection**. Choose a provider and enter credentials (or use a Wood Wide service account where supported).
  </Step>

  <Step title="Test the connection">
    Run **Test connection** before browsing or importing. Imports are blocked until the connection passes.
  </Step>

  <Step title="Browse and import">
    * **Warehouses** (BigQuery, Snowflake, PostgreSQL, Databricks): browse schemas and tables, or run a SQL query, then import.
    * **Object storage** (S3, GCS): browse folders and select CSV or Parquet files.

    Import creates a new dataset and an ingestion job. Wait for the job to finish before training.
  </Step>

  <Step title="Train and infer">
    Use the new dataset like any uploaded dataset — in the console, via the [API](/api-reference/getting-started), or [CLI](/cli). Imported datasets get the same IDs the Python SDK accepts by `dataset_id`.
  </Step>
</Steps>

***

## CLI workflow

Connections are created in the console. The CLI reuses them to browse and import without re-entering secrets.

```bash theme={null}
wwai connections list
wwai connections tables CONNECTION_ID --schema analytics
wwai connections import CONNECTION_ID \
  --mode table --schema analytics --table orders \
  --name "orders snapshot"
wwai jobs wait JOB_ID
wwai models train --dataset DATASET_ID --type prediction --label-column target --wait
```

For S3 or GCS, browse with `wwai connections objects` and import with `--mode object`.

See the [CLI page](/cli) for the full command set.

***

## Related docs

* [Datasets API](/api-reference/datasets) — manage datasets after import
* [Data preparation guide](/data-preparation-guide) — format and schema tips before training