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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

SortByKey and Collect

Many times it is useful to sort the pair RDD based on the key (for example word count which you'll see later in the chapter). In this exercise, you'll sort the pair RDD Rdd_Reduced that you created in the previous exercise into descending order and print the final output.

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

Instructions

100 XP
  • Sort the Rdd_Reduced RDD using the key in descending order.
  • Collect the contents and iterate to print the output.