Hands-On Big Data Analytics with PySpark

Hands-On Big Data Analytics with PySpark

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Overview

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs

Key Features

  • 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

Book 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.

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

Who this book is 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.

Product Details

ISBN-13: 9781838644130
Publisher: Packt Publishing
Publication date: 03/29/2019
Pages: 182
Product dimensions: 7.50(w) x 9.25(h) x 0.39(d)

About the Author

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as big data, data science, machine learning, and cloud computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the world's most popular soft drinks companies, helping each of them to better make sense of their data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence ->Action. Rudy Lai is the founder of QuantCopy, a sales acceleration start-up using AI to write sales emails to prospective customers. Prior to founding QuantCopy, Rudy ran HighDimension.IO, a machine learning consultancy, where he experienced first hand the frustrations of outbound sales and prospecting. Rudy has also spent more than 5 years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. He holds a computer science degree from Imperial College London, where he was part of the Dean's list, and received awards including the Deutsche Bank Artificial Intelligence prize. Bartłomiej Potaczek is a software engineer working for Schibsted Tech Polska and programming mostly in JavaScript. He is a big fan of everything related to the react world, functional programming, and data visualization. He founded and created InitLearn, a portal that allows users to learn to program in a pair-programming fashion. He was also involved in InitLearn frontend, which is built on the React-Redux technologies. Besides programming, he enjoys football and crossfit. Currently, he is working on rewriting the frontend for tv.nu—Sweden's most complete TV guide, with over 200 channels. He has also recently worked on technologies including React, React Router, and Redux.

Table of Contents

Table of Contents

  1. Installing Pyspark and Setting up Your Development Environment
  2. Getting Your Big Data into the Spark Environment Using RDDs
  3. Big Data Cleaning and Wrangling with Spark Notebooks
  4. Aggregating and Summarizing Data into Useful Reports
  5. Powerful Exploratory Data Analysis with MLlib
  6. Putting Structure on Your Big Data with SparkSQL
  7. Transformations and Actions
  8. Immutable Design
  9. Avoiding Shuffle and Reducing Operational Expenses
  10. Saving Data in the Correct Format
  11. Working with the Spark Key/Value API
  12. Testing Apache Spark Jobs
  13. Leveraging the Spark GraphX API

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