Load Amazon Athena data to Microsoft Fabric

Amazon Athena is an interactive query service that enables analysis of data directly in Amazon S3 using standard SQL. Everything needed to build a working Amazon Athena → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Amazon Athena to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Amazon Athena API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Amazon Athena API at a glance

These values come from the Amazon Athena API reference — the authoritative source if anything here looks out of date.

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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 file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Amazon Athena data can I load into Microsoft Fabric?

These are the Amazon Athena endpoints dlt can load into Microsoft Fabric:

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How do I load only new Amazon Athena records?

Amazon Athena exposes NextToken on ListQueryExecutions, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "list_query_executions", "endpoint": {
    "path": "ListQueryExecutions",
    "data_selector": "QueryExecutionIds",
    "incremental": {"cursor_path": "NextToken", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Amazon Athena pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading StartQueryExecution and GetQueryResults from the Amazon Athena API into Microsoft Fabric:

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


if __name__ == "__main__":
    load_amazon_athena_to_fabric()

Run it with python amazon_athena_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Amazon Athena data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

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

SQL:

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SELECT * FROM amazon_athena_data.list_query_executions LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Amazon Athena to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Amazon Athena loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Amazon Athena data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack