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title: 'Data Accelerators' sidebar_label: 'Data Accelerators' description: '' image: /img/og/data-accelerators.png sidebar_position: 2 pagination_prev: null pagination_next: null

Data sourced by Data Connectors can be locally materialized and accelerated using a Data Accelerator.

A Data Accelerator will query/fetch data from a connected data source and store/update it locally in an embedded acceleration engine, such as DuckDB or SQLite. To set data refresh behavior, such as refreshing data on an interval see Data Refresh.

Dataset acceleration is enabled by setting the acceleration configuration. E.g.

For the complete reference specification see datasets.

By default, datasets will be locally materialized using in-memory Arrow records.

A choice of DuckDB, SQLite, or PostgreSQL engines can be used to materialize data, in-memory, on disk, or in attached databases.

Supported Data Accelerators include:

Name	Description	Status	Engine Modes
arrow	In-Memory Arrow Records	Stable	memory
cayenne	Cayenne	Alpha (v1.9.0-rc.1+)	file
duckdb	Embedded DuckDB	Stable	memory, file
postgres	Attached PostgreSQL	Release Candidate	N/A
sqlite	Embedded SQLite	Release Candidate	memory, file

Data Types

Data Accelerators may not support all possible Apache Arrow data types. For complete compatibility, see specifications.

:::warning[Memory Considerations]

When accelerating a dataset using mode: memory (the default), some or all of the dataset is loaded into memory. Ensure sufficient memory is available, including overhead for queries and the runtime, especially with concurrent queries.

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

:::

Data Accelerator Docs

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