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As parallel data analysis has become increasingly common, practitioners in many fields have sought easier tools for this task. Apache Spark has quickly emerged as one of the most popular tools for this purpose, extending and generalizing MapReduce. Spark offers three main benefits. First, it is easy to use—you can develop applications on your laptop, using a high-level API that lets you focus on the content of your computation. Second, Spark is fast, enabling interactive use and complex algorithms. And third, Spark is a general engine, allowing you to combine multiple types of computations (e.g., SQL queries, text processing and machine learning) that might previously have required learning different engines. These features make Spark an excellent starting point to learn about big data in general.

This introductory book is meant to get you up and running with Spark quickly. You'll learn how to learn how to download and run Spark on your laptop and use it interactively to learn the API. Once there, we'll cover the details of available operations and distributed execution. Finally, you'll get a tour of the higher-level libraries built into Spark, including libraries for machine learning, stream processing, graph analytics and SQL. We hope that this book gives you the tools to quickly tackle data analysis problems, whether you do so on one machine or hundreds.

Audience

This book targets Data Scientists and Engineers. We chose these two groups because they have the most to gain from using Spark to expand the scope of problems they can solve. Spark's rich collection of data focused libraries (like MLlib) make it easy for data scientists to go beyond problems that fit on single machine while making use of their statistical background. Engineers, meanwhile, will learn how to write general-purpose distributed programs in Spark and operate production applications. Engineers and data scientists will both learn different details from this book, but will both be able to apply Spark to solve large distributed problems in their respective fields.

Data scientists focus on answering questions or building models from data. They often have a statistical or math background and some familiarity with tools like Python, R and SQL. We have made sure to include Python, and wherever possible SQL, examples for all our material, as well as an overview of the machine learning and advanced analytics libraries in Spark. If you are a data scientist, we hope that after reading this book you will be able to use the same mathematical approaches to solving problems, except much faster and on a much larger scale.

The second group this book targets is software engineers who have some experience with Java, Python or another programming language. If you are an engineer, we hope that this book will show you how to set up a Spark cluster, use the Spark shell, and write Spark applications to solve parallel processing problems. If you are familiar with Hadoop, you have a bit of a head start on figuring out how to interact with HDFS and how to manage a cluster, but either way, we will cover basic distributed execution concepts.

Regardless of whether you are a data analyst or engineer, to get the most of this book you should have some familiarity with one of Python, Java, Scala, or a similar language. We assume that you already have a solution for storing your data and we cover how to load and save data from many common ones, but not how to set them up. If you don't have experience with one of those languages, don't worry, there are excellent resources available to learn these. We call out some of the books available in Supporting Books.

How This Book is Organized

The chapters of this book are laid out in such a way that you should be able to go through the material front to back. At the start of each chapter, we will mention which sections of the chapter we think are most relevant to data scientists and which sections we think are most relevant for engineers. That said, we hope that all the material is accessible to readers of either background.

The first two chapters will get you started with getting a basic Spark installation on your laptop and give you an idea of what you can accomplish with Apache Spark. Once we've got the motivation and setup out of the way, we will dive into the Spark Shell, a very useful tool for development and prototyping. Subsequent chapters then cover the Spark programming interface in detail, how applications execute on a cluster, and higher-level libraries available on Spark such as Spark SQL and MLlib.

Supporting Books

If you are a data scientist and don't have much experience with Python, the Learning Python book is an excellent introduction.
If you are an engineer and after reading this book you would like to expand your data analysis skills, Machine Learning for Hackers and Doing Data Science are excellent books from O'Reilly.
This book is intended to be accessible to beginners. We do intend to release a deep dive follow-up for those looking to gain a more thorough understanding of Spark's internals.

Code Examples

All of the code examples found in this book are on GitHub. You can examine them and check them out from https://github.com/databricks/learning-spark. Code examples are provided in Java, Scala, and Python.

Our Java examples are written to work with Java version 6 and higher. Java 8 introduces a new syntax called "lambdas" that makes writing inline functions much easier, which can simplify Spark code. We have chosen not to take advantage of this syntax in most of our examples, as most organizations are not yet using Java 8. If you would like to try Java 8 syntax, you can see the Databricks blog post on this topic(https://www.iteblog.com/archives/1065).

Early Release Status and Feedback

This is an early release copy of Learning Spark, and as such we are still working on the text, adding code examples, and writing some of the later chapters. Although we hope that the book is useful in its current form, we would greatly appreciate your feedback so we can improve it and make the best possible finished product. The authors and editors can be reached at book-feedback@databricks.com.

The authors would like to thank the reviewers who offered feedback so far: Juliet Hougland, Andrew Gal, Michael Gregson, Stephan Jou, Josh Mahonin, and Mike Patterson.

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