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  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

RDDs from Parallelized collections

Resilient Distributed Dataset (RDD) is the basic abstraction in Spark. It is an immutable distributed collection of objects. Since RDD is a fundamental and backbone data type in Spark, it is important that you understand how to create it. In this exercise, you'll create your first RDD in PySpark from a collection of words.

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

Instructions

100 XP
  • Create a RDD named RDD from a Python list of words.
  • Confirm the object created is RDD.