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Hands-On Big Data Analytics with PySpark
Hands-On Big Data Analytics with PySpark

Hands-On Big Data Analytics with PySpark: Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Profile Icon Rudy Lai Profile Icon Bartłomiej Potaczek
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€10.99 €15.99
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.8 (5 Ratings)
eBook Mar 2019 182 pages 1st Edition
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€10.99 €15.99
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Arrow left icon
Profile Icon Rudy Lai Profile Icon Bartłomiej Potaczek
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€10.99 €15.99
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.8 (5 Ratings)
eBook Mar 2019 182 pages 1st Edition
eBook
€10.99 €15.99
Paperback
€19.99
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Free Trial
Renews at €18.99p/m
eBook
€10.99 €15.99
Paperback
€19.99
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Renews at €18.99p/m

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Hands-On Big Data Analytics with PySpark

Getting Your Big Data into the Spark Environment Using RDDs

Primarily, this chapter will provide a brief overview of how to get your big data into the Spark environment using resilient distributed datasets (RDDs). We will be using a wide array of tools to interact with and modify this data so that useful insights can be extracted. We will first load the data on Spark RDDs and then carry out parallelization with Spark RDDs.

In this chapter, we will cover the following topics:

  • Loading data onto Spark RDDs
  • Parallelization with Spark RDDs
  • Basics of RDD operation

Loading data on to Spark RDDs

In this section, we are going to look at loading data on to Spark RDDs, and will cover the following topics:

  • The UCI machine learning data repository
  • Getting data from the repository to Python
  • Getting data into Spark

Let's start with an overview of the UCI machine learning data repository.

The UCI machine learning repository

We can access the UCI machine learning repository by navigating to https://archive.ics.uci.edu/ml/. So, what is the UCI machine learning repository? UCI stands for the University of California Irvine machine learning repository, and it is a very useful resource for getting open source and free datasets for machine learning. Although PySpark's main issue or solution...

Parallelization with Spark RDDs

Now that we know how to create RDDs within the text file that we received from the internet, we can look at a different way to create this RDD. Let's discuss parallelization with our Spark RDDs.

In this section, we will cover the following topics:

  • What is parallelization?
  • How do we parallelize Spark RDDs?

Let's start with parallelization.

What is parallelization?

The best way to understand Spark, or any language, is to look at the documentation. If we look at Spark's documentation, it clearly states that, for the textFile function that we used last time, it reads the text file from HDFS.

On the other hand, if we look at the definition of parallelize, we can see that this is...

Basics of RDD operation

Let's now go through some RDD operational basics. The best way to understand what something does is to look at the documentation so that we can get a rigorous understanding of what a function performs.

The reason why this is very important is that the documentation is the golden source of how a function is defined and what it is designed to be used as. By reading the documentation, we make sure that we are as close to the source as possible in our understanding. The link to the relevant documentation is https://spark.apache.org/docs/latest/rdd-programming-guide.html.

So, let's start with the map function. The map function returns an RDD by applying the f function to each element of this RDD. In other words, it works the same as the map function we see in Python. On the other hand, the filter function returns a new RDD containing only the elements...

Summary

In this chapter, we learned how to load data on Spark RDDs and also covered parallelization with Spark RDDs. We had a brief overview of the UCI machine learning repository before loading the data. We had an overview of the basic RDD operations, and also checked the functions from the official documentation.

In the next chapter, we will cover big data cleaning and data wrangling.

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Key benefits

  • Work with large amounts of agile data using distributed datasets and in-memory caching
  • Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3
  • Employ the easy-to-use PySpark API to deploy big data Analytics for production

Description

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.

Who is this book for?

This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.

What you will learn

  • Get practical big data experience while working on messy datasets
  • Analyze patterns with Spark SQL to improve your business intelligence
  • Use PySpark s interactive shell to speed up development time
  • Create highly concurrent Spark programs by leveraging immutability
  • Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation
  • Re-design your jobs to use reduceByKey instead of groupBy
  • Create robust processing pipelines by testing Apache Spark jobs

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Publication date : Mar 29, 2019
Length: 182 pages
Edition : 1st
Language : English
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Publication date : Mar 29, 2019
Length: 182 pages
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Language : English
ISBN-13 : 9781838648831
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Apache
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Languages :
Concepts :
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Table of Contents

14 Chapters
Installing Pyspark and Setting up Your Development Environment Chevron down icon Chevron up icon
Getting Your Big Data into the Spark Environment Using RDDs Chevron down icon Chevron up icon
Big Data Cleaning and Wrangling with Spark Notebooks Chevron down icon Chevron up icon
Aggregating and Summarizing Data into Useful Reports Chevron down icon Chevron up icon
Powerful Exploratory Data Analysis with MLlib Chevron down icon Chevron up icon
Putting Structure on Your Big Data with SparkSQL Chevron down icon Chevron up icon
Transformations and Actions Chevron down icon Chevron up icon
Immutable Design Chevron down icon Chevron up icon
Avoiding Shuffle and Reducing Operational Expenses Chevron down icon Chevron up icon
Saving Data in the Correct Format Chevron down icon Chevron up icon
Working with the Spark Key/Value API Chevron down icon Chevron up icon
Testing Apache Spark Jobs Chevron down icon Chevron up icon
Leveraging the Spark GraphX API Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Half star icon Empty star icon Empty star icon Empty star icon 1.8
(5 Ratings)
5 star 0%
4 star 0%
3 star 20%
2 star 40%
1 star 40%
dmdgirl Jun 03, 2022
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This book goes over some really great concepts.I was in the University of Miami Master’s in Analytics program and this book did help with basic skills but does not go into enough detail for deep learning and machine learning in pyspark.
Amazon Verified review Amazon
Kindle Customer Aug 12, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Has some good information, but it needs better review and editing. If you follow along with the book there are multiple errors and in Chapter 7 it suddenly switches to Scala code with no explanation.
Amazon Verified review Amazon
Cristian Fabian Ortiz Muñoz Jun 11, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I dislike because it has a very light content.
Amazon Verified review Amazon
Michael Meier Mar 01, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Sehr schlecht gemacht, kein strukturierter Aufbau, fehlerhaft, nicht zu empfehlen
Amazon Verified review Amazon
Devang Jul 19, 2022
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Book content is very poor. Not at all worth the cost
Amazon Verified review Amazon
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