Apache Spark 2.x Cookbook

Over 70 recipes to help you use Apache Spark as your single big data computing platform and master its librariesAbout This Book* This book contains recipes on how to use Apache Spark as a unified compute engine* Cover how to connect various source systems to Apache Spark* Covers various parts of machine learning including supervised/unsupervised learning & recommendation enginesWho This Book Is ForThis book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. Anyone who is using Spark (or is planning to) will benefit from this book. The book assumes you have a basic knowledge of Scala as a programming language. What You Will Learn* Install and configure Apache Spark with various cluster managers & on AWS* Set up a development environment for Apache Spark including Databricks Cloud notebook* Find out how to operate on data in Spark with schemas* Get to grips with real-time streaming analytics using Spark Streaming & Structured Streaming* Master supervised learning and unsupervised learning using MLlib* Build a recommendation engine using MLlib* Graph processing using GraphX and GraphFrames libraries* Develop a set of common applications or project types, and solutions that solve complex big data problemsIn DetailWhile Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark. Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting. Style and approachThis book is packed with intuitive recipes supported with line-by-line explanations to help you understand Spark 2.x's real-time processing capabilities and deploy scalable big data solutions. This is a valuable resource for data scientists and those working on large-scale data projects.

Cover -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Apache Spark -- Introduction -- Leveraging Databricks Cloud -- How to do it ... -- How it works ... -- Cluster -- Notebook -- Table -- Library -- Deploying Spark using Amazon EMR -- What it represents is much bigger than what it looks -- EMR's architecture -- How to do it ... -- How it works ... -- EC2 instance types -- T2 -- Free Tier Burstable (EBS only) -- M4 -- General purpose (EBS only) -- C4 -- Compute optimized -- X1 -- Memory optimized -- R4 -- Memory optimized -- P2 -- General purpose GPU -- I3 -- Storage optimized -- D2 -- Storage optimized -- Installing Spark from binaries -- Getting ready -- How to do it ... -- Building the Spark source code with Maven -- Getting ready -- How to do it ... -- Launching Spark on Amazon EC2 -- Getting ready -- How to do it ... -- See also -- Deploying Spark on a cluster in standalone mode -- Getting ready -- How to do it ... -- How it works ... -- See also -- Deploying Spark on a cluster with Mesos -- How to do it ... -- Deploying Spark on a cluster with YARN -- Getting read…