YugabyteDB Python API Docs | dltHub

YugabyteDB is a distributed SQL database (Postgres-compatible) providing high-performance, cloud-native transactional and analytical workloads; its management APIs (YugabyteDB Anywhere / Managed APIs) allow programmatic deployment and administration of universes, clusters, nodes, backups, and monitoring. The REST API base URL is For YugabyteDB Anywhere (self-managed): https://<yba-host>/api For Yugabyte Managed/Cloud APIs (public): https://api.yugabyte.com (consult provider docs for exact path/versions) and All requests require an API token sent in a request header (X-AUTH-YW-API-TOKEN)..

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 YugabyteDB data in under 10 minutes.

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What data can I load from YugabyteDB?

Here are some of the endpoints you can load from YugabyteDB:

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How do I authenticate with the YugabyteDB API?

The API uses an API token associated with a YugabyteDB Anywhere user. Include the token in the X-AUTH-YW-API-TOKEN HTTP header on every request (Content-Type: application/json for JSON requests/responses).

1. Get your credentials

1. Log into your YugabyteDB Anywhere web UI. 2) Click the user/person icon (top-right) -> Profile -> General. 3) If API Token is blank, click Generate Key. 4) Copy the API token (generating a new one invalidates previous tokens). For managed/cloud, obtain API key from cloud account UI per provider docs.

2. Add them to .dlt/secrets.toml

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[sources.yugabytedb_api_source]
api_token = "your_api_token_here"

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 YugabyteDB 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 yugabytedb_api_pipeline.py

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

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Pipeline yugabytedb_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset yugabytedb_api_data
The duckdb destination used duckdb:/yugabytedb_api.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 yugabytedb_api_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 universes and nodes from the YugabyteDB 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 yugabytedb_api_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "For YugabyteDB Anywhere (self-managed): https://<yba-host>/api For Yugabyte Managed/Cloud APIs (public): https://api.yugabyte.com (consult provider docs for exact path/versions)",
            "auth": {
                "type": "api_key",
                "api_token": api_token,
            },
        },
        "resources": [
            {"name": "universes", "endpoint": {"path": "customers/{customerUUID}/universes", "data_selector": "data"}},
            {"name": "nodes", "endpoint": {"path": "customers/{customerUUID}/providers/{providerUUID}/nodes", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="yugabytedb_api_pipeline",
        destination="duckdb",
        dataset_name="yugabytedb_api_data",
    )
    load_info = pipeline.run(yugabytedb_api_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("yugabytedb_api_pipeline").dataset()
sessions_df = data.universes.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM yugabytedb_api_data.universes LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("yugabytedb_api_pipeline").dataset()
data.universes.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 YugabyteDB 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 you get 401 Unauthorized: ensure X-AUTH-YW-API-TOKEN header contains a valid, unexpired token. Generating a new token invalidates prior tokens.

Resource not found / UUID usage

Many endpoints require customer, provider, zone, or universe UUIDs in the path. If you receive 404, verify you’re using the correct UUID (obtain via listing endpoints or from the web UI URL path).

Background tasks and polling

Mutating operations often return a task handle; poll the customer tasks endpoint to check completion status.

Error response format

Errors return JSON with fields such as error, errorJson, and success=false. Standard HTTP codes (400,401,403,404,409,5xx) are used.

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