Introduction to PySpark

It covers SparkSessions, RDDs and DataFrames, filtering and joining large datasets, and querying with Spark SQL. The key thing it teaches, beyond syntax, is when distributed processing is actually the right tool: Spark earns its complexity only when data outgrows a single machine, and a big part of using it well is recognising that threshold rather than reaching for it by default. Get that judgment right and PySpark becomes the workhorse for genuinely large data.

It's a practical entry for people who already know some Python and SQL, and overkill if you only ever work with data that fits comfortably in pandas. You'll need DataCamp's subscription (about $14/month billed annually, first chapter free); the certificate confirms you did the work rather than certifying skill. Spark's core model has stayed stable for years (as of 2026).

Introduction to PySpark covers Apache Spark's distributed processing model from the ground up: setting up SparkSessions, working with RDDs and DataFrames, filtering and joining large datasets, and querying with Spark SQL using familiar SQL syntax. It closes with performance topics — caching, broadcast joins, and execution plan basics — that matter once you're working at real big-data scale.