Spark In Action Pdf Download

Spark in Action
by Petar Zečević, Marko Bonaći

Summary

Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Big data systems distribute datasets across clusters of machines, making it a challenge to efficiently query, stream, and interpret them. Spark can help. It is a processing system designed specifically for distributed data. It provides easy-to-use interfaces, along with the performance you need for production-quality analytics and machine learning. Spark 2 also adds improved programming APIs, better performance, and countless other upgrades.

About the Book

Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. You’ll get comfortable with the Spark CLI as you work through a few introductory examples. Then, you’ll start programming Spark using its core APIs. Along the way, you’ll work with structured data using Spark SQL, process near-real-time streaming data, apply machine learning algorithms, and munge graph data using Spark GraphX. For a zero-effort startup, you can download the preconfigured virtual machine ready for you to try the book’s code.

What’s Inside

  • Updated for Spark 2.0
  • Real-life case studies
  • Spark DevOps with Docker
  • Examples in Scala, and online in Java and Python

About the Reader

Written for experienced programmers with some background in big data or machine learning.

About the Authors

Petar Zečević and Marko Bonaći are seasoned developers heavily involved in the Spark community.

Table of Contents

PART 1 – FIRST STEPS

  • Introduction to Apache Spark
  • Spark fundamentals
  • Writing Spark applications
  • The Spark API in depth
  • PART 2 – MEET THE SPARK FAMILY
  • Sparkling queries with Spark SQL
  • Ingesting data with Spark Streaming
  • Getting smart with MLlib
  • ML: classification and clustering
  • Connecting the dots with GraphX
  • PART 3 – SPARK OPS
  • Running Spark
  • Running on a Spark standalone cluster
  • Running on YARN and Mesos
  • PART 4 – BRINGING IT TOGETHER
  • Case study: real-time dashboard
  • Deep learning on Spark with H2O

Spark GraphX in Action
by Michael S. Malak, Robin East

Summary

Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial then teaches you how to configure GraphX and how to use it interactively. Along the way, you’ll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

GraphX is a powerful graph processing API for the Apache Spark analytics engine that lets you draw insights from large datasets. GraphX gives you unprecedented speed and capacity for running massively parallel and machine learning algorithms.

About the Book

Spark GraphX in Action begins with the big picture of what graphs can be used for. This example-based tutorial teaches you how to use GraphX interactively. You’ll start with a crystal-clear introduction to building big data graphs from regular data, and then explore the problems and possibilities of implementing graph algorithms and architecting graph processing pipelines. Along the way, you’ll collect practical techniques for enhancing applications and applying machine learning algorithms to graph data.

What’s Inside

  • Understanding graph technology
  • Using the GraphX API
  • Developing algorithms for big graphs
  • Machine learning with graphs
  • Graph visualization

About the Reader

Readers should be comfortable writing code. Experience with Apache Spark and Scala is not required.

About the Authors

Michael Malak has worked on Spark applications for Fortune 500 companies since early 2013. Robin East has worked as a consultant to large organizations for over 15 years and is a data scientist at Worldpay.

Table of Contents

PART 1 SPARK AND GRAPHS

  • Two important technologies: Spark and graphs
  • GraphX quick start
  • Some fundamentals
  • PART 2 CONNECTING VERTICES
  • GraphX Basics
  • Built-in algorithms
  • Other useful graph algorithms
  • Machine learning
  • PART 3 OVER THE ARC
  • The missing algorithms
  • Performance and monitoring
  • Other languages and tools

Mastering Apache Spark
by Mike Frampton

Gain expertise in processing and storing data by using advanced techniques with Apache Spark

About This Book

  • Explore the integration of Apache Spark with third party applications such as H20, Databricks and Titan
  • Evaluate how Cassandra and Hbase can be used for storage
  • An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities

Who This Book Is For

If you are a developer with some experience with Spark and want to strengthen your knowledge of how to get around in the world of Spark, then this book is ideal for you. Basic knowledge of Linux, Hadoop and Spark is assumed. Reasonable knowledge of Scala is expected.

What You Will Learn

  • Extend the tools available for processing and storage
  • Examine clustering and classification using MLlib
  • Discover Spark stream processing via Flume, HDFS
  • Create a schema in Spark SQL, and learn how a Spark schema can be populated with data
  • Study Spark based graph processing using Spark GraphX
  • Combine Spark with H20 and deep learning and learn why it is useful
  • Evaluate how graph storage works with Apache Spark, Titan, HBase and Cassandra
  • Use Apache Spark in the cloud with Databricks and AWS

In Detail

Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at unprecedented speeds, is easy to use and offers a rich set of data transformations.

This book aims to take your limited knowledge of Spark to the next level by teaching you how to expand Spark functionality. The book commences with an overview of the Spark eco-system. You will learn how to use MLlib to create a fully working neural net for handwriting recognition. You will then discover how stream processing can be tuned for optimal performance and to ensure parallel processing. The book extends to show how to incorporate H20 for machine learning, Titan for graph based storage, Databricks for cloud-based Spark. Intermediate Scala based code examples are provided for Apache Spark module processing in a CentOS Linux and Databricks cloud environment.

Style and approach

This book is an extensive guide to Apache Spark modules and tools and shows how Spark’s functionality can be extended for real-time processing and storage with worked examples.


Learning Spark
by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

  • Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
  • Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib
  • Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm
  • Learn how to deploy interactive, batch, and streaming applications
  • Connect to data sources including HDFS, Hive, JSON, and S3
  • Master advanced topics like data partitioning and shared variables

Learning PySpark
by Tomasz Drabas, Denny Lee

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

About This Book

  • Learn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0
  • Develop and deploy efficient, scalable real-time Spark solutions
  • Take your understanding of using Spark with Python to the next level with this jump start guide

Who This Book Is For

If you are a Python developer who wants to learn about the Apache Spark 2.0 ecosystem, this book is for you. A firm understanding of Python is expected to get the best out of the book. Familiarity with Spark would be useful, but is not mandatory.

What You Will Learn

  • Learn about Apache Spark and the Spark 2.0 architecture
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectively
  • Read, transform, and understand data and use it to train machine learning models
  • Build machine learning models with MLlib and ML
  • Learn how to submit your applications programmatically using spark-submit
  • Deploy locally built applications to a cluster

In Detail

Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. This book will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark 2.0 architecture and how to set up a Python environment for Spark.

You will get familiar with the modules available in PySpark. You will learn how to abstract data with RDDs and DataFrames and understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and MLlib, graph processing using GraphFrames, and polyglot persistence using Blaze. Finally, you will learn how to deploy your applications to the cloud using the spark-submit command.

By the end of this book, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications.

Style and approach

This book takes a very comprehensive, step-by-step approach so you understand how the Spark ecosystem can be used with Python to develop efficient, scalable solutions. Every chapter is standalone and written in a very easy-to-understand manner, with a focus on both the hows and the whys of each concept.


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