Preface
Apache Spark has captured the imagination of the analytics and big data developers, rightfully so. In a nutshell, Spark enables distributed computing at scale in the lab or in production. Until now, the collect-store-transform pipeline was distinct from the data science Reason-Model pipeline , which was again distinct from the deployment of the analytics and machine learning models. Now with Spark and technologies such as Kafka, we can seamlessly span the data management and data science pipelines. Moreover, now we can build data science models on larger datasets and need not just sample data. And whatever models we build can be deployed into production (with added work from engineering on the “ilities”, of course). It is our hope that this book will enable a data engineer to get familiar with the fundamentals of the Spark platform as well as provide hands-on experience of some of the advanced capabilities.
What this book covers
Chapter 1, Installing Spark and Setting Up Your Cluster, details some common methods for setting up Spark.
Chapter 2, Using the Spark Shell, introduces the command line for Spark. The shell is good for trying out quick program snippets or just figuring out the syntax of a call interactively.
Chapter 3, Building and Running a Spark Application, covers the ways for compiling Spark applications.
Chapter 4, Creating a SparkSession Object, describe the programming aspects of the connection to a spark server regarding the Spark session and the enclosed spark context.
Chapter 5, Loading and Saving Data in Spark, deals with how we can get data in and out of a spark environment.
Chapter 6, Manipulating Your RDD, describes how to program Resilient Distributed Datasets, which is the fundamental data abstraction layer in Spark that makes all the magic possible.
Chapter 7, Spark 2.0 Concepts, is a short, interesting chapter that discusses the evolution of Spark and the concepts underpinning the Spark 2.0 release, which is a major milestone.
Chapter 8 , Spark SQL, deals with the SQL interface in Spark. Spark SQL probably is the most widely used feature.
Chapter 9, Foundations of Datasets/DataFrames – The Proverbial Workhorse for DataScientists, is another interesting chapter, which introduces the Datasets/DataFrames that are added in the Spark 2.0 release.
Chapter 10, Spark with Big Data, describes the interfaces with Parquet and HBase.
Chapter 11, Machine Learning with Spark ML Pipelines, is my favorite chapter. We talk about regression, classification, clustering, and recommendation in this chapter. This is probably the largest chapter in this book. If you are stranded in a remote island and could take only one chapter with you, this should be the one!
Chapter 12, GraphX, talks about an important capability, processing graphs at scale, and also discusses interesting algorithms such as PageRank.
What you need for this book
Like any development platform, learning to develop systems with Spark takes trial and error. Writing programs, encountering errors, and agonizing over pesky bugs are all part of the process. We assume a basic level of programming – Python or Java and experience in working with operating system commands. We have kept the examples simple and to the point. In terms of resources, we do not assume any esoteric equipment for running the examples and developing code. A normal development machine is enough.
Who this book is for
Data scientists and data engineers who are new to Spark will benefit from this book. Our goal in developing this book is to give an in-depth, hands-on, end-to-end knowledge of Apache Spark 2. We have kept it simple and short so that one can get a good introduction in a short period of time. Folks who have an exposure to big data and analytics will recognize the patterns and the pragmas. Having said that, anyone who wants to understand distributed programming will benefit from working through the examples and reading the book.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The hallmark of a MapReduce system is this: map and reduce, the two primitives."
A block of code is set as follows:
<dependency> <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.11</version> <scope>test</scope> </dependency>
Any command-line input or output is written as follows:
./ec2/spark-ec2 -i ~/spark-keypair.pem launch myfirstsparkcluster --resume
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "From Spark 2.0.0 onwards, they have changed the packaging, so we have to include spark-2.0.0/assembly/target/scala-2.11/jars in Add External Jars…."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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