Realhub Python API Docs | dltHub

Realhub is a property marketing and campaign management platform providing a REST API to access campaigns, bookings, orders, users and related agency/provider data. The REST API base URL is https://realhub.realbase.io/api/v2 and All requests require either an API key header for server‑to‑server access or a user auth token for user‑level access..

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 Realhub data in under 10 minutes.

---

What data can I load from Realhub?

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

---

How do I authenticate with the Realhub API?

Server integrations use an API key sent in the x-api-token header. User‑level integrations authenticate by POSTing username/password to /users/auth.json to receive an auth token which is sent in the x-auth-token header on subsequent requests.

1. Get your credentials

1. Contact Realhub support or your Realhub account representative to request API access. 2) In the Realhub admin dashboard (or via Realhub support), create or request an API key for the desired environment (staging vs production). 3) For user tokens, call POST /users/auth.json with username and password to receive an auth_token; do not store plain passwords.

2. Add them to .dlt/secrets.toml

Copy
[sources.realhub_source]
api_key = "your_realhub_api_key_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 Realhub 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:

Copy
python realhub_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline realhub_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.

---

Python pipeline example

This example loads campaigns and bookings from the Realhub API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def realhub_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://realhub.realbase.io/api/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "campaigns", "endpoint": {"path": "campaigns.json"}},
            {"name": "bookings", "endpoint": {"path": "bookings.json"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="realhub_pipeline",
        destination="duckdb",
        dataset_name="realhub_data",
    )
    load_info = pipeline.run(realhub_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.

---

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):

Copy
import dlt

data = dlt.pipeline("realhub_pipeline").dataset()
sessions_df = data.campaigns.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM realhub_data.campaigns LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("realhub_pipeline").dataset()
data.campaigns.df().head()

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

---

What destinations can I load Realhub 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.

---

Troubleshooting

Authentication failures

If you receive 401 Unauthorized: verify you are using the correct environment (staging vs production) and the correct header. For server API keys send x-api-token: YOUR_API_KEY. For user tokens send x-auth-token: AUTH_TOKEN obtained from POST /users/auth.json. 401 indicates wrong or missing API key/token.

Error codes

Realhub returns common HTTP error codes: 400 Bad Request, 401 Unauthorized (incorrect API key), 403 Forbidden (endpoint not permitted for user), 500 Internal Server Error. Handle them according to standard REST practices and retry 5xx errors with backoff.

Pagination & limits

Some endpoints accept limit/query parameters (e.g., data_store_items.json supports limit with default 100, max 100). Many list endpoints return a full JSON array; check documentation per endpoint for supported filtering and limit params. When responses contain a top‑level array, iterate through results; when responses return an object (for reports) the records are under the data key.

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

---

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.

Copy
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