Transforming API JSON Data into Structured Tables with PySpark

Working with semi-structured data is a common challenge in data engineering. Many APIs return data in JSON format, but for analytics and processing, we often need to transform it into structured tables.

With PySpark's from_json function, we can easily parse JSON and convert it into a tabular format. Here’s a practical example of how to pull JSON data from an API and structure it in PySpark:

Step 1: Fetch JSON Data from an API

We use Python's requests library to retrieve data from an API.

Step 2: Process JSON with PySpark

Now, we transform this JSON into a structured PySpark DataFrame.

Output:

Why This Matters?

• APIs return JSON, but analytics requires structured data.
• PySpark's from_json efficiently maps JSON fields to table columns.
• This method scales to large datasets in distributed environments.

?? By using this approach, we can easily integrate API data into ETL pipelines, making it available for analysis and reporting.

Have you used PySpark to handle API data before? Let’s discuss in the comments! ??