Mastering Complex JSON ETL with PySpark: Dynamic Schema Inference & Data Sharding Explained
Mastering Complex JSON ETL with PySpark: Dynamic Schema Inference & Data Sharding Explained
GitHub: https://lnkd.in/d_YTT9E8
Why Create This Video?
In this tutorial, I demonstrate a real-world scenario where data engineers often encounter complex JSON files with nested structures. Data engineers need to quickly and efficiently extract, transform, and load (ETL) data from such files into analytics platforms or data lakes. In this video, you'll learn:
Dynamic Data Ingestion: How to read and validate complex JSON files.
Schema Inference vs. Explicit Schemas: When and how to leverage Spark's dynamic schema inference.
Data Sharding: How to split the data into two logical DataFrames—one for user profiles and one for transactions—preserving relationships via user_id.
Production Best Practices: Tips on modularizing code, error handling, and performance tuning for scalable data pipelines.
JSON Structure Explanation
The JSON file in this tutorial is a JSON array containing multiple user records. Each record includes:
user_id: A unique identifier for each user (string).
profile: A nested JSON object that contains:
name: The user's name (string).
age: The user's age (number).
address: A nested object with:
street: Street address (string).
city: City name (string).
zip: ZIP code (string).
transactions: An array of transaction objects, where each transaction has:
tx_id: A transaction identifier (string).
amount: The transaction amount (number).
date: The transaction date (string).