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title: 'Spice.ai Data Connector' sidebar_label: 'Spice.ai Data Connector' description: 'Spice.ai Data Connector Documentation' pagination_next: null

The Spice.ai Data Connector enables federated SQL query across datasets in the Spice.ai Cloud Platform. Access to these datasets requires a free Spice.ai account.

Configuration

Secrets

Secrets will be written to a .env file by using the spice login command and logging in with an active Spice AI account. Learn more about the Env Secret Store.

• api_key: A Spice.ai API key.
• token: An active personal access token that is configured when logging in to spice via spice login.

Parameters

from

The Spice.ai Cloud Platform dataset URI. To query a dataset in a public Spice.ai App, use the format spice.ai/<org>/<app>/datasets/<dataset_name>.

name

The dataset name. This will be used as the table name within Spice. The dataset name cannot be a reserved keyword.

params

The Spice.ai Cloud Platform data connector can be configured by providing the following params. Use the secret replacement syntax to load the secret from a secret store, e.g. ${secrets:SPICEAI_API_KEY}.

Parameter Name	Description
spiceai_api_key	The Spice.ai Cloud Platform API key to connect with.

Example

Full Configuration Example

Cookbook

• A cookbook recipe to configure Spice.ai Cloud Platform as a data connector in Spice. Spice.ai Cloud Platform Data Connector

Limitations

• The Spice.ai Data Connector is subject to a maximum limit of 1000 requests per connection, after which the connection is reset by the Spice Cloud Platform. If the error message Connection is reset by the server. Please retry the request. is encountered or the spiceai-retryable metadata appears in the response, the query should be retried.

:::warning[Memory Considerations]

When using the Spice.ai Data Connector without acceleration, part of the query execution will be in memory if federating across different Spice Cloud Platform apps. Ensure sufficient memory is available, including overhead for queries and the runtime, especially with concurrent queries.

Memory limitations can be mitigated by storing acceleration data on disk, which is supported by duckdb and sqlite accelerators by specifying mode: file.