AWS SDK for pandas Python API Docs | dltHub

AWS SDK for pandas (awswrangler) is a Python library that provides high-level helpers to read/write data between pandas and many AWS services (S3, Glue, Athena, OpenSearch, Redshift, DynamoDB, Timestream, etc.). The REST API base URL is There is no single global REST base URL for aws-sdk-pandas itself. For OpenSearch-related operations the REST endpoints are the target OpenSearch domain endpoint, e.g. https://<DOMAIN-ENDPOINT> (OpenSearch domain endpoint; operations map to paths like /_search and /_bulk). and All requests to AWS services use AWS credentials (SigV4 via boto3); aws-sdk-pandas functions accept a boto3_session or use the default AWS SDK credential resolution..

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 AWS SDK for pandas data in under 10 minutes.

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What data can I load from AWS SDK for pandas?

Here are some of the endpoints you can load from AWS SDK for pandas:

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How do I authenticate with the AWS SDK for pandas API?

aws-sdk-pandas uses boto3 sessions and AWS SigV4 signing. Supply valid AWS credentials via boto3_session parameter, environment variables, AWS credentials file, or IAM role  no manual Bearer header required; the library uses boto3/opensearchpy clients which sign requests automatically.

1. Get your credentials

1. Open AWS Console  IAM  Users  Create user.
2. Select programmatic access and attach policies granting access to target services (e.g., AmazonOpenSearchServiceFullAccess, S3, Glue as needed).
3. After creation, copy Access key ID and Secret access key.
4. Configure credentials locally via AWS CLI: aws configure (enter access key, secret, default region) or set env vars AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, or create a profile in ~/.aws/credentials.

2. Add them to .dlt/secrets.toml

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[sources.aws_sdk_for_pandas_source]
aws_access_key_id = "YOUR_ACCESS_KEY_ID"
aws_secret_access_key = "YOUR_SECRET_ACCESS_KEY"
aws_region = "us-west-2"
aws_profile = "your_profile_name"

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 Workbench:

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dlt 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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dlt 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 AWS SDK for pandas 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 aws_sdk_for_pandas_pipeline.py

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

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Pipeline aws_sdk_for_pandas_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset aws_sdk_for_pandas_data
The duckdb destination used duckdb:/aws_sdk_for_pandas.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 aws_sdk_for_pandas_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 search and index_df from the AWS SDK for pandas 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 aws_sdk_for_pandas_source(boto3_session=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "There is no single global REST base URL for aws-sdk-pandas itself. For OpenSearch-related operations the REST endpoints are the target OpenSearch domain endpoint, e.g. https://<DOMAIN-ENDPOINT> (OpenSearch domain endpoint; operations map to paths like /_search and /_bulk).",
            "auth": {
                "type": "aws_sigv4",
                "aws_access_key_id": boto3_session,
            },
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "_search", "data_selector": "hits.hits"}},
            {"name": "index_df", "endpoint": {"path": "_bulk", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="aws_sdk_for_pandas_pipeline",
        destination="duckdb",
        dataset_name="aws_sdk_for_pandas_data",
    )
    load_info = pipeline.run(aws_sdk_for_pandas_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("aws_sdk_for_pandas_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM aws_sdk_for_pandas_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("aws_sdk_for_pandas_pipeline").dataset()
data.search.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 AWS SDK for pandas 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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Troubleshooting

Authentication failures

If boto3 cannot find credentials, awswrangler/opensearch.connect will fail. Verify AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY, aws_region or configured profile; run aws sts get-caller-identity to confirm active credentials. Ensure the IAM user/role has required OpenSearch permissions.

Bulk indexing errors

Bulk responses include an 'errors' boolean and an 'items' array with per-item statuses. Inspect response['items'] to find failed items and their error.reason; retry failed items after correcting mapping/type issues.

Rate limits and throttling

OpenSearch may return 429 Too Many Requests for heavy_bulk loads. Backoff and retry; reduce concurrency (use_threads param in index_df) or batch size to mitigate throttling.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

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

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

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime 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