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Author: Michael Malak
Publisher: Simon and Schuster
ISBN: 1638353301
Category : Computers
Languages : en
Pages : 422
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Book Description
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
Author: Michael Malak
Publisher: Simon and Schuster
ISBN: 1638353301
Category : Computers
Languages : en
Pages : 422
Get Book Here
Book Description
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
Author: Michael East
Publisher:
ISBN:
Category : Computer network protocols
Languages : en
Pages : 280
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Book Description
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. 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.
Author: Petar Zecevic
Publisher: Manning
ISBN: 9781617292606
Category : Computers
Languages : en
Pages : 0
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Book Description
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
Author: Srini Penchikala
Publisher: Lulu.com
ISBN: 1387659952
Category : Computers
Languages : en
Pages : 106
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Book Description
Apache Spark is a popular open-source big-data processing framework thatÕs built around speed, ease of use, and unified distributed computing architecture. Not only it supports developing applications in different languages like Java, Scala, Python, and R, itÕs also hundred times faster in memory and ten times faster even when running on disk compared to traditional data processing frameworks. Whether you are currently working on a big data project or interested in learning more about topics like machine learning, streaming data processing, and graph data analytics, this book is for you. You can learn about Apache Spark and develop Spark programs for various use cases in big data analytics using the code examples provided. This book covers all the libraries in Spark ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark ML, and Spark GraphX.
Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1491943173
Category : Computers
Languages : en
Pages : 356
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Book Description
Apache Spark is amazing when everything clicks. But if you haven’t seen the performance improvements you expected, or still don’t feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, you’ll also learn how to make it sing. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages
Author: Marko Bonaci
Publisher: Simon and Schuster
ISBN: 1638351074
Category : Computers
Languages : en
Pages : 707
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Book Description
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
Author: Bill Chambers
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912294
Category : Computers
Languages : en
Pages : 594
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Book Description
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
Author: Sourav Gulati
Publisher: Packt Publishing Ltd
ISBN: 178712942X
Category : Computers
Languages : en
Pages : 338
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Book Description
Unleash the data processing and analytics capability of Apache Spark with the language of choice: Java About This Book Perform big data processing with Spark—without having to learn Scala! Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics Go beyond mainstream data processing by adding querying capability, Machine Learning, and graph processing using Spark Who This Book Is For If you are a Java developer interested in learning to use the popular Apache Spark framework, this book is the resource you need to get started. Apache Spark developers who are looking to build enterprise-grade applications in Java will also find this book very useful. What You Will Learn Process data using different file formats such as XML, JSON, CSV, and plain and delimited text, using the Spark core Library. Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming Library Learn SQL schema creation and the analysis of structured data using various SQL functions including Windowing functions in the Spark SQL Library Explore Spark Mlib APIs while implementing Machine Learning techniques to solve real-world problems Get to know Spark GraphX so you understand various graph-based analytics that can be performed with Spark In Detail Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone. The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications. Style and approach This practical guide teaches readers the fundamentals of the Apache Spark framework and how to implement components using the Java language. It is a unique blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.
Author: Rindra Ramamonjison
Publisher: Packt Publishing Ltd
ISBN: 1784398950
Category : Computers
Languages : en
Pages : 148
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Book Description
Build, process and analyze large-scale graph data effectively with Spark About This Book Find solutions for every stage of data processing from loading and transforming graph data to Improve the scalability of your graphs with a variety of real-world applications with complete Scala code. A concise guide to processing large-scale networks with Apache Spark. Who This Book Is For This book is for data scientists and big data developers who want to learn the processing and analyzing graph datasets at scale. Basic programming experience with Scala is assumed. Basic knowledge of Spark is assumed. What You Will Learn Write, build and deploy Spark applications with the Scala Build Tool. Build and analyze large-scale network datasets Analyze and transform graphs using RDD and graph-specific operations Implement new custom graph operations tailored to specific needs. Develop iterative and efficient graph algorithms using message aggregation and Pregel abstraction Extract subgraphs and use it to discover common clusters Analyze graph data and solve various data science problems using real-world datasets. In Detail Apache Spark is the next standard of open-source cluster-computing engine for processing big data. Many practical computing problems concern large graphs, like the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. Apache Spark GraphX API combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. This book will teach the user to do graphical programming in Apache Spark, apart from an explanation of the entire process of graphical data analysis. You will journey through the creation of graphs, its uses, its exploration and analysis and finally will also cover the conversion of graph elements into graph structures. This book begins with an introduction of the Spark system, its libraries and the Scala Build Tool. Using a hands-on approach, this book will quickly teach you how to install and leverage Spark interactively on the command line and in a standalone Scala program. Then, it presents all the methods for building Spark graphs using illustrative network datasets. Next, it will walk you through the process of exploring, visualizing and analyzing different network characteristics. This book will also teach you how to transform raw datasets into a usable form. In addition, you will learn powerful operations that can be used to transform graph elements and graph structures. Furthermore, this book also teaches how to create custom graph operations that are tailored for specific needs with efficiency in mind. The later chapters of this book cover more advanced topics such as clustering graphs, implementing graph-parallel iterative algorithms and learning methods from graph data. Style and approach A step-by-step guide that will walk you through the key ideas and techniques for processing big graph data at scale, with practical examples that will ensure an overall understanding of the concepts of Spark.
Author: Muhammad Asif Abbasi
Publisher: Packt Publishing Ltd
ISBN: 1785889583
Category : Computers
Languages : en
Pages : 349
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Book Description
Learn about the fastest-growing open source project in the world, and find out how it revolutionizes big data analytics About This Book Exclusive guide that covers how to get up and running with fast data processing using Apache Spark Explore and exploit various possibilities with Apache Spark using real-world use cases in this book Want to perform efficient data processing at real time? This book will be your one-stop solution. Who This Book Is For This guide appeals to big data engineers, analysts, architects, software engineers, even technical managers who need to perform efficient data processing on Hadoop at real time. Basic familiarity with Java or Scala will be helpful. The assumption is that readers will be from a mixed background, but would be typically people with background in engineering/data science with no prior Spark experience and want to understand how Spark can help them on their analytics journey. What You Will Learn Get an overview of big data analytics and its importance for organizations and data professionals Delve into Spark to see how it is different from existing processing platforms Understand the intricacies of various file formats, and how to process them with Apache Spark. Realize how to deploy Spark with YARN, MESOS or a Stand-alone cluster manager. Learn the concepts of Spark SQL, SchemaRDD, Caching and working with Hive and Parquet file formats Understand the architecture of Spark MLLib while discussing some of the off-the-shelf algorithms that come with Spark. Introduce yourself to the deployment and usage of SparkR. Walk through the importance of Graph computation and the graph processing systems available in the market Check the real world example of Spark by building a recommendation engine with Spark using ALS. Use a Telco data set, to predict customer churn using Random Forests. In Detail Spark juggernaut keeps on rolling and getting more and more momentum each day. Spark provides key capabilities in the form of Spark SQL, Spark Streaming, Spark ML and Graph X all accessible via Java, Scala, Python and R. Deploying the key capabilities is crucial whether it is on a Standalone framework or as a part of existing Hadoop installation and configuring with Yarn and Mesos. The next part of the journey after installation is using key components, APIs, Clustering, machine learning APIs, data pipelines, parallel programming. It is important to understand why each framework component is key, how widely it is being used, its stability and pertinent use cases. Once we understand the individual components, we will take a couple of real life advanced analytics examples such as 'Building a Recommendation system', 'Predicting customer churn' and so on. The objective of these real life examples is to give the reader confidence of using Spark for real-world problems. Style and approach With the help of practical examples and real-world use cases, this guide will take you from scratch to building efficient data applications using Apache Spark. You will learn all about this excellent data processing engine in a step-by-step manner, taking one aspect of it at a time. This highly practical guide will include how to work with data pipelines, dataframes, clustering, SparkSQL, parallel programming, and such insightful topics with the help of real-world use cases.
