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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, then browse schemas or objects and import into a dataset for training and inference. Supported providers include BigQuery, Snowflake, PostgreSQL, Databricks, S3, and GCS.

Add a connection

Create and test a connection in the console.

CLI import

Import from an existing connection with wwai connections.

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Console workflow

1

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).
2

Test the connection

Run Test connection before browsing or importing. Imports are blocked until the connection passes.
3

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.
4

Train and infer

Use the new dataset like any uploaded dataset — in the console, via the API, or CLI. Imported datasets get the same IDs the Python SDK accepts by dataset_id.

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CLI workflow

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