How to load data from Polygon Stock API to Databricks Lakehouse

Ensure you have access to both the Polygon Stock API and your Databricks Lakehouse environment. You'll need an API key from Polygon, and you'll need to configure your Databricks workspace. Make sure you have Python installed on your local machine or the environment where you'll run your scripts.

Use Python to send HTTP requests to the Polygon Stock API. You can use the `requests` library to interact with the API. First, construct the API endpoint URL with your API key and the specific data you'd like to retrieve (e.g., stock prices, aggregates).

```python
import requests

api_key = 'YOUR_API_KEY'
url = f'https://api.polygon.io/v2/aggs/ticker/AAPL/prev?apiKey={api_key}'

response = requests.get(url)
data = response.json()
```

Convert the JSON response from the API into a format suitable for loading into Databricks. You might need to clean or transform the data, depending on its structure and your requirements. Python's `pandas` library is useful for this purpose.

```python
import pandas as pd

df = pd.json_normalize(data, 'results')
```

Set up your Databricks cluster if you haven't already. Ensure that the cluster is running and that you have the necessary permissions to create databases and tables. You can do this from the Databricks UI.

Save the transformed data to a CSV file locally and then upload it to DBFS. You can use Databricks CLI or the Databricks UI to upload files to DBFS.

```python
df.to_csv('stock_data.csv', index=False)
```

Then, use the Databricks CLI to upload:
```bash
databricks fs cp stock_data.csv dbfs:/FileStore/stock_data.csv
```

Use Databricks notebooks to read the CSV file from DBFS and load it into a table in your Lakehouse. You can do this using PySpark within Databricks.

```python
df_spark = spark.read.csv('/FileStore/stock_data.csv', header=True, inferSchema=True)
df_spark.write.format('delta').mode('overwrite').saveAsTable('stock_data_table')
```

Validate that the data has been loaded correctly by querying the Databricks table. Once confirmed, consider automating the entire process by scheduling a job in Databricks that periodically fetches new data from the Polygon Stock API and updates the table.

```sql
SELECT * FROM stock_data_table LIMIT 10
```

Use Databricks Jobs or Databricks Workflows to automate the execution of your notebook, scheduling it to run at your desired frequency.