How to load data from News API to Databricks Lakehouse
Learn how to use Airbyte to synchronize your News API data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Set Up News API Access
Begin by registering for an API key with the News API provider. This key will be used to authenticate your requests. Make sure to read the API documentation to understand the available endpoints and request limits.
Step 2: Configure Python Environment
Set up a Python environment on your local machine or directly in Databricks. You can use a simple script to manage API requests. Ensure you have necessary packages like `requests`, `pandas`, and `json` installed using pip.
Step 3: Fetch Data from News API
Write a Python script to send HTTP GET requests to the News API endpoints using the `requests` library. Parse the JSON response to extract the desired data fields. You can structure this data into a pandas DataFrame for easier manipulation.
Step 4: Clean and Transform Data
Perform any necessary data cleaning and transformation within your pandas DataFrame. This might include handling missing values, normalizing text, or converting data types to ensure compatibility with Databricks Lakehouse.
Step 5: Set Up Databricks Environment
Log into your Databricks account and navigate to your workspace. Create a new cluster if one is not already running. Ensure your cluster has the necessary libraries installed, such as Spark and Pandas API on Spark.
Step 6: Upload Data to Databricks File System (DBFS)
Utilize the Databricks web interface or CLI to upload your cleaned data from your local machine to the Databricks File System. You can export your DataFrame to a CSV or JSON file and then use the `dbutils.fs.cp` command to move it to DBFS.
Step 7: Load Data into Databricks Lakehouse
Use Databricks notebooks to read the uploaded file from DBFS into a Spark DataFrame. Use the `spark.read` method with appropriate options for CSV or JSON files. Once the data is loaded into a Spark DataFrame, save it to a Delta table in your Databricks Lakehouse using the `write.format("delta").save()` method.
By following these steps, you can effectively move data from a News API to a Databricks Lakehouse without relying on third-party connectors or integrations.