> For the complete documentation index, see [llms.txt](https://docs.zerve.ai/zerve-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zerve.ai/zerve-docs/notebooks/blocks/sql-query.md).

# SQL query

Query a data connection, or a dataframe from another block, with SQL.

A **query** block runs a query and saves the result as a dataframe, which the blocks after it can use. You don't need to install a database driver or write connection code.

## Querying a data connection

Choose one of the project's [data connections](/zerve-docs/data-and-assets/connections.md), such as Snowflake or PostgreSQL, and write your SQL.

For a [Weaviate](/zerve-docs/data-and-assets/connections/weaviate.md) vector database, write a GraphQL query instead.

## Querying a dataframe

You can also run SQL directly on a pandas dataframe. Connect a Python block that creates the dataframe to the query block, and query the dataframe as if it were a table.

This is useful for quick aggregations and joins on data you've already loaded.

## Using the result

The query's result is saved as a dataframe, which you can use in Python or R blocks connected after it. See [Language interoperability](/zerve-docs/notebooks/language-interoperability.md).


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