Load AirDNA data in Python using dltHub

Build a AirDNA-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete AirDNA data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def airdna_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.airdna.co/api/enterprise/v2/",
            "auth": {
                "type": "bearer",
                "token": "api_key",
            },
        },
        "resources": [
            listing, market
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='airdna_pipeline',
        destination='duckdb',
        dataset_name='airdna_data', 
    )
    # Load the data
    load_info = pipeline.run(airdna_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from airdna’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Listings: Retrieve comparable listings, listing details, and pricing strategies for specific properties
  • Markets: Access market-level data and information for specific market IDs
  • Smart Rates: Get pricing strategy recommendations and dynamic rate suggestions for listings
  • Submarkets: Fetch submarket data and analytics within larger market regions

You will then debug the AirDNA pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with AirDNA support.

    dlt init dlthub:airdna duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for AirDNA API, as specified in @airdna-docs.yaml 
Start with endpoint(s) listing and market and skip incremental loading for now. 
Place the code in airdna_pipeline.py and name the pipeline airdna_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python airdna_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    All requests to AirDNA's APIs require an Authorization HTTP header with a Bearer token. Obtain an API Key by contacting sales, then include it in the Authorization header as "Bearer <API_KEY>".

Header name: Authorization Header value format: Bearer a1b2c3d4e5f6g7h8i9j0k1l2 (where the string after "Bearer " is your API Key) Acquisition: Contact AirDNA sales to obtain an API Key Note: API Key must not be expired and your account must be authorized to access the specific API Package for each endpoint

To get the appropriate API keys, please visit the original source at airdna.redoc.ly.
If you want to protect your environment secrets in a production environment, look into [setting up credentials with dlt](https://dlthub.com/docs/walkthroughs/add_credentials).

4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

```shell
python airdna_pipeline.py
```

If your pipeline runs correctly, you’ll see something like the following:

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

5. 📈 Debug your pipeline and data with the Pipeline Dashboard

Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:
- Pipeline overview: State, load metrics
- Data’s schema: tables, columns, types, hints
- You can query the data itself

```shell
dlt pipeline airdna_pipeline show 
```

6. 🐍 Build a Notebook with data explorations and reports

With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.


```python
import dlt

data = dlt.pipeline("airdna_pipeline").dataset()

get listing table as Pandas frame

data.listing.df().head() ```

Extra resources:

Next steps