Apache Spark Fundamentals: Distributed Data Processing

<p>ParcelPulse's delivery reports ran fine on a laptop and a few hundred lines of pandas -- until three new hubs and a holiday surge turned a two-minute job into a memory crash. This project follows data analyst Meera Iyer as she rebuilds that pipeline in Apache Spark: understanding why single-machine tools hit a wall, how Spark's driver-executor architecture and DataFrame API actually work, how to write real transformations against production-shaped data, and how to diagnose and fix the shuffles, skew, and anti-patterns that turn a working Spark job into a fifty-five-minute one. Every lesson builds on the same growing company and ships real, runnable PySpark code.</p>

Tools: Apache Spark, PySpark, Python, Parquet, SQL