Mastering PySpark for Complex Business ETL Tasks: A Comprehensive Guide

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Introduction:

In today’s data-driven world, businesses rely heavily on data to make informed decisions. Extract, Transform, Load (ETL) processes play a crucial role in data management, ensuring that data is accurately and efficiently processed from various sources to a target system. PySpark, with its powerful DataFrame and SQL APIs, has become a go-to tool for data engineers and professionals. This article provides a comprehensive guide to 20 complex business ETL tasks using PySpark, covering scenarios from data ingestion to data auditing.

1. Data Ingestion from Multiple Sources

Business Justification: Ingesting data from various sources like CSV, JSON, and SQL Server into a single PySpark DataFrame is crucial for data analysis and processing.

Sample Code:

from pyspark.sql import SparkSession

# Create a SparkSession
spark = SparkSession.builder.appName("Data Ingestion").getOrCreate()

# Ingest data from CSV
csv_df = spark.read.csv("data.csv", header=True, inferSchema=True)

# Ingest data from JSON
json_df = spark.read.json("data.json")

# Ingest data from SQL Server
sql_df = spark.read.format("jdbc") \
  .option("url", "jdbc:sqlserver://localhost:1433;databaseName=MyDB") \
  .option("query"…