Amazon Athena Python API Docs | dltHub

Amazon Athena is an interactive query service that enables analysis of data directly in Amazon S3 using standard SQL. The REST API base URL is https://athena.<region>.amazonaws.com and all requests require AWS Signature Version 4 authentication.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Amazon Athena data in under 10 minutes.

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What data can I load from Amazon Athena?

Here are some of the endpoints you can load from Amazon Athena:

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How do I authenticate with the Amazon Athena API?

Amazon Athena uses AWS Signature Version 4 (SigV4) for authentication, which requires signing HTTP requests using AWS access keys. Requests must include the Authorization header containing the signature, as well as the x-amz-date and x-amz-content-sha256 headers.

1. Get your credentials

To obtain credentials for Amazon Athena, navigate to the AWS Management Console and open the IAM dashboard. Create an IAM user or role with the necessary permissions (e.g., 'AmazonAthenaFullAccess' and S3 access). Under the 'Security credentials' tab for an IAM user, generate an 'Access key' and 'Secret access key'. For production environments, it is recommended to use IAM roles (via instance profiles or environment variables) or temporary credentials provided by AWS STS instead of static long-term access keys.

2. Add them to .dlt/secrets.toml

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[sources.amazon_athena_source]
aws_access_key_id = "YOUR_ACCESS_KEY"
aws_secret_access_key = "YOUR_SECRET_KEY"
aws_session_token = "YOUR_SESSION_TOKEN"
region_name = "us-east-1"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI harness:

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dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlthub ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Amazon Athena API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python amazon_athena_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline amazon_athena_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset amazon_athena_data
The duckdb destination used duckdb:/amazon_athena.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline amazon_athena_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads StartQueryExecution and GetQueryResults from the Amazon Athena API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def amazon_athena_source(aws_access_key_id_aws_secret_access_key_region_name_and_optionally_aws_session_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://athena.<region>.amazonaws.com",
            "auth": {"type": "api_key", "api_key": aws_access_key_id_aws_secret_access_key_region_name_and_optionally_aws_session_token, "name": "Authorization", "location": "header"},
        },
        "resources": [
            {"name": "list_query_executions", "endpoint": {"path": "ListQueryExecutions", "data_selector": "QueryExecutionIds"}},
            {"name": "list_named_queries", "endpoint": {"path": "ListNamedQueries", "data_selector": "NamedQueryIds"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="amazon_athena_pipeline",
        destination="duckdb",
        dataset_name="amazon_athena_data",
    )
    load_info = pipeline.run(amazon_athena_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("amazon_athena_pipeline").dataset()
sessions_df = data.list_query_executions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM amazon_athena_data.list_query_executions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("amazon_athena_pipeline").dataset()
data.list_query_executions.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Amazon Athena data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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dlthub ai toolkit data-exploration install
dlthub ai toolkit dlthub-platform install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack