Getting Started with PySpark and RDDs

Embark on your PySpark adventure by mastering Resilient Distributed Datasets (RDDs). Create and transform data efficiently, unlocking the basics needed to handle large datasets and set the stage for exciting data processing challenges ahead.

Lessons and practices

Lesson 1: Creating Your First RDD with SparkSession

Building Your First PySpark RDD

Optimize SparkSession Configuration

Fix Bugs in PySpark Script

Create and Collect the RDD

Build a PySpark Application

Lesson 2: Loading and Analyzing File Data with RDDs

Complete the RDD Operations with File

Switch File Format in PySpark

Troubleshooting RDD File Loading

Complete RDD Operations from File

Master RDD File Operations

Lesson 3: Applying Map Transformations to RDDs

Complete the PySpark Map Transformation

Cube RDD Elements with Map

Capitalizing Words with Map Transformation

Master Map Transformations with Usernames

Lesson 4: Filtering RDD Elements Based on Conditions

Inserting PySpark Filter Method

Filter Odd Numbers in PySpark

Filtering Names RDD in PySpark

Filter Logs Within RDD

Lesson 5: Transforming and Saving Data with RDDs

Complete the Code for Saving Data

Modify RDD Data Reading Pattern

Master RDD Data Partitioning

Process High-Value Sales Effortlessly

Interested in this course? Learn and practice with Cosmo!

Practice is how you turn knowledge into actual skills.