How to load data from Exchange Rates Api to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Exchange Rates Api data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Set Up Databricks Environment
Begin by setting up your Databricks environment. This involves creating a new Databricks account if you don't have one, and setting up a new workspace. Within the workspace, create a new cluster where you'll run your notebooks and scripts. Ensure the cluster is running with a compatible Python version since you'll be utilizing Python for API interaction.
Step 2: Access Exchange Rates API
Identify the exchange rates API you will be using. Ensure you have an API key if required. Familiarize yourself with the API documentation to understand the endpoints and data formats available. Use Python to write a script that can send HTTP requests to the API. Libraries like `requests` are ideal for making these calls.
Step 3: Fetch Data Using Python
Within a Databricks notebook, write a Python script using the `requests` library to fetch data from the exchange rates API. You'll need to send a GET request to the API endpoint and handle the response. Make sure to parse the JSON response into a Python dictionary or list for easy manipulation.
```python
import requests
api_url = "https://api.exchangerate-api.com/v4/latest/USD"
response = requests.get(api_url)
data = response.json()
```
Step 4: Transform Data for Lakehouse Compatibility
Once you've fetched the data, process it into a format suitable for storage in the Databricks Lakehouse. This might involve converting the JSON data into a Pandas DataFrame. Pandas will allow you to manipulate and prepare your data, ensuring that it can be easily converted into a Spark DataFrame later.
```python
import pandas as pd
df = pd.DataFrame(data['rates'].items(), columns=['Currency', 'Rate'])
```
Step 5: Convert Pandas DataFrame to Spark DataFrame
Use Databricks' built-in capabilities to convert the Pandas DataFrame to a Spark DataFrame. This conversion is necessary because Spark DataFrames are the primary abstraction for working with structured data in Databricks.
```python
spark_df = spark.createDataFrame(df)
```
Step 6: Store Data in Databricks Lakehouse
Save the Spark DataFrame to the Databricks Lakehouse. Choose an appropriate storage format like Delta Lake for added features like ACID transactions and scalable metadata handling. You can save the data to a predefined location in your Databricks File System (DBFS).
```python
spark_df.write.format("delta").mode("overwrite").save("/mnt/datalake/exchange_rates")
```
Step 7: Schedule Regular Data Ingestion
To keep your data updated, automate the data fetching and processing by scheduling the notebook as a job in Databricks. Set an appropriate frequency for the job based on how often the exchange rates API updates its data.
```plaintext
- In Databricks workspace, go to 'Jobs'.
- Create a new job, specify the notebook, and set the schedule.
- Monitor the job runs to ensure successful data ingestion.
```
By following these steps, you will effectively transfer data from an exchange rates API to a Databricks Lakehouse without relying on third-party connectors or integrations.