Pubnub Python API Docs | dltHub

PubNub is a real-time data streaming and messaging platform providing publish/subscribe messaging, presence, message history, serverless functions, and account management APIs. The REST API base URL is Admin API: https://admin-api.pubnub.com/v2; Admin Portal API: https://admin.pubnub.com/api; REST SDK (public API endpoints): https://ps.pndsn.com (SDK endpoints) and many endpoints use channel-specific paths. and Admin/API management endpoints require API Key in Authorization header; REST SDK endpoints use publish/subscribe keys as query params and optional auth query param for Access Manager; Functions API uses session token/X-Session-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 Pubnub data in under 10 minutes.

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What data can I load from Pubnub?

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

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How do I authenticate with the Pubnub API?

Admin API requires Authorization: YOUR_API_KEY_HERE and PubNub-Version headers. REST SDK uses uuid and subscribe/publish keys in the URL path, with an optional auth query parameter for Access Manager. Functions/API portal uses X-Session-Token or a session token from responses.

1. Get your credentials

To obtain Admin API credentials: login to Admin Portal -> Organization Settings -> API Management/Service Integrations -> create Service Integration -> copy API Key. For REST SDK keys: create an app/keyset in Admin Portal to get publishKey and subscribeKey.

2. Add them to .dlt/secrets.toml

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[sources.pubnub_source]
api_key = "your_admin_api_key_here"; publish_key = "pub-c-..."; subscribe_key = "sub-c-..."; session_token = "your_session_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 Pubnub 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 pubnub_pipeline.py

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

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Pipeline pubnub_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pubnub_data
The duckdb destination used duckdb:/pubnub.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 pubnub_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 keysets and actions from the Pubnub 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 pubnub_source(api_key, publish_key, subscribe_key, session_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "Admin API: https://admin-api.pubnub.com/v2; Admin Portal API: https://admin.pubnub.com/api; REST SDK (public API endpoints): https://ps.pndsn.com (SDK endpoints) and many endpoints use channel-specific paths.",
            "auth": {
                "type": "api_key (Admin), api_key (publish/subscribe keys), bearer-like session token for Functions",
                "token (session token) for Functions, api_key for Admin": api_key, publish_key, subscribe_key, session_token,
            },
        },
        "resources": [
            {"name": "keysets", "endpoint": {"path": "v2/keysets", "data_selector": "keysets"}},
            {"name": "actions", "endpoint": {"path": "v1/history-actions/sub-key/{sub_key}/channel/{channel}", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pubnub_pipeline",
        destination="duckdb",
        dataset_name="pubnub_data",
    )
    load_info = pipeline.run(pubnub_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("pubnub_pipeline").dataset()
sessions_df = data.actions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pubnub_data.actions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pubnub_pipeline").dataset()
data.actions.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 Pubnub 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

For Admin API include Authorization header with API Key. Missing/invalid key returns 401/403. Ensure PubNub-Version header is present.

Rate limits

Admin API rate limited (120 requests/min). Exceeding returns 429 with X-RateLimit-* headers indicating limits and reset.

Access Manager / signatures

Endpoints protected by Access Manager require either auth query param (token) or signature+timestamp; missing/invalid values return 403.

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