Book Image

Hands-On Big Data Analytics with PySpark

By : Rudy Lai, Bartłomiej Potaczek
Book Image

Hands-On Big Data Analytics with PySpark

By: Rudy Lai, Bartłomiej Potaczek

Overview of this book

Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs. You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark. By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
Table of Contents (15 chapters)

Saving data in plain text format

In this section, we will learn how to save data in plain text format. The following topics will be covered:

  • Saving data in plain text format
  • Loading plain text data
  • Testing

We will save our data in plain text format and investigate how to save it into the Spark directory. We will then load the plain text data, and then test and save it to check whether we can yield the same results code. This is our SavePlainText.scala file:

package com.tomekl007.chapter_4

import java.io.File

import com.tomekl007.UserTransaction
import org.apache.spark.sql.SparkSession
import org.apache.spark.{Partitioner, SparkContext}
import org.scalatest.{BeforeAndAfterEach, FunSuite}
import org.scalatest.Matchers._

import scala.reflect.io.Path

class SavePlainText extends FunSuite with BeforeAndAfterEach{
val spark: SparkContext = SparkSession.builder().master("local[2]&quot...