Load Entrata data in Python using dltHub

Entrata API provides programmatic access to the Entrata property management platform for managing property, resident, lease, maintenance, payment, and financial data. The REST API base URL is https://{subdomain}.entrata.com/api and Supports Basic authentication or OAuth 2.0 Bearer tokens in the Authorization header..

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 add "dlt[hub]" and start loading Entrata data in under 10 minutes.

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What data can I load from Entrata?

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

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How do I authenticate with the Entrata API?

Entrata supports both Basic authentication (using base64 encoded username

in the Authorization header) and OAuth 2.0 (using a Bearer token in the Authorization header). OAuth is primarily used for App Store integrations, while Basic auth is used for standard web service requests.

1. Get your credentials

To obtain API credentials, log in to your Entrata property management account with administrative privileges. Navigate to the Apps menu and select API Access. Click Add API User to create a new user profile for your integration. After creating the user, assign the necessary permissions or web services required for your specific data needs. Once configured, navigate to the Credentials tab to copy your generated API Username and Password. Alternatively, depending on your organization's setup, you may access credentials via the Entrata Developer Portal by selecting your registered application and navigating to My Subscriptions to retrieve an API Key. Ensure you also note your unique Entrata subdomain (e.g., 'yourcompany.entrata.com'), which is required for API requests.

2. Add them to .dlt/secrets.toml

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[sources.entrata_source]
entrata_username = "your_api_username"
entrata_password = "your_api_password"
entrata_subdomain = "your_company_subdomain"
# Or if using an API Key
entrata_api_key = "your_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.

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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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Entrata 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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uv run python entrata_pipeline.py

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

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Pipeline entrata_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset entrata_data
The duckdb destination used duckdb:/entrata.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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uv run dlthub 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 getStatus and getProperties from the Entrata 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 entrata_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{subdomain}.entrata.com/api",
            "auth": {"type": "bearer", "token": access_token},
        },
        "resources": [
            {"name": "leases", "endpoint": {"path": "leases", "data_selector": "response.result.data"}},
            {"name": "leads", "endpoint": {"path": "leads", "data_selector": "response.result.data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="entrata_pipeline",
        destination="duckdb",
        dataset_name="entrata_data",
    )
    load_info = pipeline.run(entrata_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("entrata_pipeline").dataset()
sessions_df = data.leases.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM entrata_data.leases LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("entrata_pipeline").dataset()
data.leases.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 Entrata 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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