Complex Nested JSON Flattening Pipeline

How do we build a complete pipeline extracting metadata and exploding nested JSON records into flat tabular columns?

Advanced⏱ 3 Minutes⭐ 820 XP

🤔 THE QUESTION

How do we build a complete pipeline extracting metadata and exploding nested JSON records into flat tabular columns?

💡 WHAT IS IT?

A flattening pipeline selecting metadata fields, exploding record arrays, and unpacking nested child properties.

🎯 WHAT IS IT USED FOR?

Silver-layer lakehouse ingestion of raw JSON REST API batches into flat dimensional tables.

💻 EXAMPLE

from pyspark.sql.functions import col, explode

flat_df = raw_json_df \
    .select(
        col("batch_id"),
        col("metadata.source").alias("source"),
        explode(col("payload.records")).alias("record")
    ) \
    .select(
        col("batch_id"),
        col("source"),
        col("record.customer_id").alias("customer_id"),
        col("record.amount").alias("amount")
    )

🎯 Mission Objectives

Practice typing production-grade PySpark code for Complex Nested JSON Flattening Pipeline.

✓ Extract batch metadata
✓ Explode nested payload record arrays
✓ Unpack leaf properties into flat tabular columns

🚀 Start Mission

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