Mixing DataFrame API & SQL Syntax

How do we seamlessly combine SQL queries with downstream DataFrame filter operations in a unified pipeline?

Advanced⏱ 2 Minutes⭐ 650 XP

🤔 THE QUESTION

How do we seamlessly combine SQL queries with downstream DataFrame filter operations in a unified pipeline?

💡 WHAT IS IT?

Querying temporary views with spark.sql() and immediately chaining DataFrame methods (.filter, .select) on the result.

🎯 WHAT IS IT USED FOR?

Leveraging SQL for complex aggregations and DataFrame APIs for programmatic downstream transformations.

💻 EXAMPLE

df.createOrReplaceTempView("orders")
gold_df = spark.sql("""
    SELECT customer_id, SUM(amount) AS total_spend
    FROM orders
    GROUP BY customer_id
""").filter("total_spend > 5000")

🎯 Mission Objectives

Practice typing production-grade PySpark code for Mixing DataFrame API & SQL Syntax.

✓ Register orders temporary view
✓ Execute SQL aggregation query
✓ Chain DataFrame filter on SQL result

🚀 Start Mission

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