1. Learn
    2. /
  2. Courses
    3. /
  3. Big Data Fundamentals with PySpark
  • 1

    Introduction to Big Data analysis with Spark

    Free

    This chapter introduces the exciting world of Big Data, as well as the various concepts and different frameworks for processing Big Data. You will understand why Apache Spark is considered the best framework for BigData.

  • 2

    Programming in PySpark RDD’s

    The main abstraction Spark provides is a resilient distributed dataset (RDD), which is the fundamental and backbone data type of this engine. This chapter introduces RDDs and shows how RDDs can be created and executed using RDD Transformations and Actions.

  • 3

    PySpark SQL & DataFrames

    In this chapter, you'll learn about Spark SQL which is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as a distributed SQL query engine. This chapter shows how Spark SQL allows you to use DataFrames in Python.

  • 4

    Machine Learning with PySpark MLlib

    PySpark MLlib is the Apache Spark scalable machine learning library in Python consisting of common learning algorithms and utilities. Throughout this last chapter, you'll learn important Machine Learning algorithms. You will build a movie recommendation engine and a spam filter, and use k-means clustering.

Initializing

Exercise

Map and Collect

The main method with which you can manipulate data in PySpark is using map(). The map() transformation takes in a function and applies it to each element in the RDD. It can be used to do any number of things, from fetching the website associated with each URL in our collection to just squaring the numbers. In this simple exercise, you'll use map() transformation to cube each number of the numbRDD RDD that you've created earlier. Next, you'll store all the elements in a variable and finally print the output.

Remember, you already have a SparkContext sc, and numbRDD available in your workspace.

Instructions

100 XP
  • Create map() transformation that cubes all of the numbers in numbRDD.
  • Collect the results in a numbers_all variable.
  • Print the output from numbers_all variable.