Leveraging PySpark for Business Intelligence: A Comprehensive Guide

In today’s data-driven world, businesses are increasingly relying on big data technologies to extract insights and drive decision-making. Apache Spark, particularly its PySpark API, has emerged as a powerful tool for processing large datasets efficiently.

This article presents a series of end-to-end PySpark scripts that address various business scenarios, showcasing how organizations can leverage these scripts for data ingestion, transformation, analysis, and machine learning. Each script is accompanied by a detailed explanation and business justification.

1. Data Ingestion and Transformation

Script: Ingesting and Transforming Sales Data

import logging
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, to_date

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def create_spark_session():
  """Create a Spark session."""
  logger.info("Creating Spark session.")
  spark = SparkSession.builder \
      .appName("SalesDataIngestion") \
      .getOrCreate()
  return spark

def ingest_data(spark, file_path):
  """Ingest data from a CSV file."""
  logger.info(f"Ingesting data from…