> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lsapi-1779323176-451247e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Athena integration

> Integrate with the Athena document loader using LangChain Python.

> [Amazon Athena](https://aws.amazon.com/athena/) is a serverless, interactive analytics service built
> on open-source frameworks, supporting open-table and file formats. `Athena` provides a simplified,
> flexible way to analyze petabytes of data where it lives. Analyze data or build applications
> from an Amazon Simple Storage Service (S3) data lake and 30 data sources, including on-premises data
> sources or other cloud systems using SQL or Python. `Athena` is built on open-source `Trino`
> and `Presto` engines and `Apache Spark` frameworks, with no provisioning or configuration effort required.

This notebook goes over how to load documents from `AWS Athena`.

## Setting up

Follow [instructions to set up an AWS account](https://docs.aws.amazon.com/athena/latest/ug/setting-up.html).

Install a python library:

```python theme={null}
! pip install boto3
```

## Example

```python theme={null}
from langchain_community.document_loaders.athena import AthenaLoader
```

```python theme={null}
database_name = "my_database"
s3_output_path = "s3://my_bucket/query_results/"
query = "SELECT * FROM my_table"
profile_name = "my_profile"

loader = AthenaLoader(
    query=query,
    database=database_name,
    s3_output_uri=s3_output_path,
    profile_name=profile_name,
)

documents = loader.load()
print(documents)
```

Example with metadata columns

```python theme={null}
database_name = "my_database"
s3_output_path = "s3://my_bucket/query_results/"
query = "SELECT * FROM my_table"
profile_name = "my_profile"
metadata_columns = ["_row", "_created_at"]

loader = AthenaLoader(
    query=query,
    database=database_name,
    s3_output_uri=s3_output_path,
    profile_name=profile_name,
    metadata_columns=metadata_columns,
)

documents = loader.load()
print(documents)
```

***

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