Overview

Handle Big Data Like a Pro-With Python and Apache Spark Today's data is massive. Terabytes. Petabytes. If you want to work at scale, you need tools that move fast and scale even faster. Big Data with Python & Spark gives you everything you need to analyze, transform, and process massive datasets using two of the most powerful tools in data engineering. This book blends Python's flexibility with Spark's power, helping you go from raw logs to clean insights-fast. Whether you're a data analyst, engineer, or developer, this hands-on guide equips you with the knowledge to tackle real-world big data projects with confidence. What You'll Learn: How to set up Spark and PySpark environments for big data projects The fundamentals of resilient distributed datasets (RDDs) and DataFrames Data cleaning, ETL pipelines, and batch processing at scale Writing fast, efficient Spark jobs with Python Working with structured and semi-structured data: JSON, CSV, Parquet Real-world use cases in finance, retail, IoT, and web analytics Performance tuning, lazy evaluation, and memory management in Spark Running Spark on local machines, clusters, or in the cloud Visualizing massive data outputs and building summaries Whether you're processing a few gigabytes or a hundred terabytes, this book will help you write scalable, maintainable, and powerful big data pipelines. Code smarter. Analyze faster. Scale bigger.

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