diff --git a/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md b/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md index 2d839d2d3b..f3ac6f83d6 100644 --- a/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md +++ b/docs/website/docs/dlt-ecosystem/verified-sources/arrow-pandas.md @@ -13,7 +13,7 @@ or [book a call](https://calendar.app.google/kiLhuMsWKpZUpfho6) with our support ::: You can load data directly from an Arrow table or Pandas dataframe. -This is supported by all destinations, but recommended especially when using destinations that support the `parquet` foramt natively (e.g. [Snowflake](../destinations/snowflake.md) and [Filesystem](../destinations/filesystem.md)). +This is supported by all destinations, but recommended especially when using destinations that support the `parquet` file format natively (e.g. [Snowflake](../destinations/snowflake.md) and [Filesystem](../destinations/filesystem.md)). See the [destination support](#destination-support-and-fallback) section for more information. When used with a `parquet` supported destination this is a more performant way to load structured data since `dlt` bypasses many processing steps normally involved in passing JSON objects through the pipeline. @@ -151,4 +151,4 @@ pipeline.run(df.to_dict(orient='records'), table_name="orders") # yield arrow table pipeline.run(table.to_pylist(), table_name="orders") ``` -Both Pandas and Arrow allow to stream records in batches. \ No newline at end of file +Both Pandas and Arrow allow to stream records in batches.