PySpark – PySpark SQL Table Of Contents: What is PySpark SQL? Why Use PySpark SQL? Setting It Up (Step-by-Step) SQL vs DataFrame APIs (Both Supported!) Advanced Features in PySpark SQL Input Data Formats Performance Optimizations Real-World Use Cases Summary (1) What is PySpark SQL? (2) Why Use PySpark SQL? (3) Setting It Up (Step-by-Step) Step 1: Create a SparkSession from pyspark.sql import SparkSession spark = SparkSession.builder .appName("PySparkSQLDemo") .getOrCreate() Step 2: Load Data into a DataFrame df = spark.read.csv("employees.csv", header=True, inferSchema=True) df.show() Step 3: Register DataFrame as SQL Table (Temp View) df.createOrReplaceTempView("employees") Step 4: Run SQL Queries! result = spark.sql(""" SELECT

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