How to load data from Coin API to Databricks Lakehouse

Before starting, familiarize yourself with the CoinAPI documentation to understand how to fetch data using their API. Similarly, understand the data storage options and formats supported by Databricks Lakehouse. This will help in planning the data extraction and storage processes.

Obtain the API key from CoinAPI by registering on their platform. This API key is essential for authenticating and authorizing your requests to the CoinAPI service. Store this key securely, as it will be used in the API requests to fetch data.

Develop a Python script that uses the `requests` library to interact with CoinAPI. Construct the HTTP GET request with the required headers, such as the API key, to fetch the desired cryptocurrency data. For example, you can fetch exchange rates, historical data, or current prices based on your needs.

```python
import requests

def fetch_data_from_coinapi():
url = 'https://rest.coinapi.io/v1/exchangerate/BTC/USD'
headers = {'X-CoinAPI-Key': 'YOUR_API_KEY'}
response = requests.get(url, headers=headers)
data = response.json()
return data
```

Once the data is fetched, transform it into a suitable format for storage. If required, clean the data and convert it into a format like JSON or CSV, which can be easily ingested into Databricks Lakehouse. Serialization ensures that the data is in a structured form ready for storage or further processing.

Use the Databricks REST API to establish a connection with the Databricks Lakehouse. Authenticate using a personal access token. This will allow you to perform operations such as file uploads directly to the Lakehouse.

Use the Databricks REST API to upload the transformed data directly to a specified location within the Lakehouse. You can use endpoints like `/dbfs` to upload files directly to Databricks File System (DBFS). Ensure that the data is uploaded to the correct directory and that necessary permissions are set.

```python
import os

def upload_to_databricks(file_path, token):
headers = {'Authorization': f'Bearer {token}'}
with open(file_path, 'rb') as f:
data = f.read()
response = requests.post(
'https:///api/2.0/dbfs/put',
headers=headers,
files={'file': data},
data={'path': '/FileStore/my_data_file', 'overwrite': 'true'}
)
return response.status_code
```

Finally, log into your Databricks workspace and verify the data upload. Use Databricks notebooks to read the data from the specified location and perform initial checks to ensure integrity and correctness. This may include loading the data into a DataFrame and executing simple queries or analyses to confirm its accuracy.

By following these steps, you can manually move data from CoinAPI to Databricks Lakehouse without relying on third-party connectors or integrations.