A DataCamp course on Apache Spark's distributed processing model — SparkSessions, RDDs and DataFrames, filtering and joining large datasets, and querying with Spark SQL. Hands-on in Python (PySpark), it is a practical entry into big-data processing for people who know some Python and SQL.
Good for: Getting started with big-data processing using PySpark.
Less suitable if: You do not know Python/SQL or work only with small data.
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.
What you'll learn
Set up and manage SparkSessions for distributed jobs
Work with PySpark DataFrames and RDDs
Filter, group, and join large datasets efficiently
Query data using Spark SQL syntax
Use user-defined functions (UDFs) and Pandas UDFs
Apply caching and broadcast joins for performance optimization
This course includes
4h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription.
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