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Spark Graphx In Action

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Spark Graphx in Action :

Author - Michael Malak, Robin East
Publisher - Manning Publications
Pages - 225
ISBN - 9781617292521


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Detail - While graphs are often the most natural way to represent the connections among data, the complexity of large graphs makes them conceptually difficult and computationally expensive to explore, query, and analyze. GraphX, a powerful graph processing API for the Apache Spark analytics engine, makes it possible to efficiently explore and interpret large-scale graph data at near-realtime speeds. GraphX works with Spark's in-memory distributed framework to offer unprecedented speed and capacity for analyzing social media data, performing complex textual analysis, handling important machine learning algorithms, and much more. Spark GraphX in Action starts out with an overview of Apache Spark and the GraphX graph processing API. This example-based tutorial explains how to configure GraphX and use GraphX interactively. It offers a crystal-clear introduction to graph elements, which are needed to build big data graphs. Then, it explores the problems and possibilities of graph algorithm implementations. Along the way, it details 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.

Spark in Action :

Computers

Author - Petar Zecevic, Marko Bonaci
Publisher - Manning Publications
Pages - 450
ISBN - 9781617292606


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Detail - Working with big data can be complex and challenging, in part because of the multiple analysis frameworks and tools required. Apache Spark is a big data processing framework perfect for analyzing near-real-time streams and discovering historical patterns in batched data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes many specialized data processing platforms obsolete. Spark's unified framework and programming model significantly lowers the initial infrastructure investment, and Spark's core abstractions are intuitive for most Scala, Java, and Python developers. Spark in Action teaches readers to use Spark for stream and batch data processing. It starts with an introduction to the Spark architecture and ecosystem followed by a taste of Spark's command line interface. Readers then discover the most fundamental concepts and abstractions of Spark, particularly Resilient Distributed Datasets (RDDs) and the basic data transformations that RDDs provide. The first part of the book covers writing Spark applications using the the core APIs. Readers also learn how to work with structured data using Spark SQL, how to process near-real time data with Spark Streaming, how to apply machine learning algorithms with Spark MLlib, how to apply graph algorithms on graph-shaped data using Spark GraphX, and an introduction to Spark clustering. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Graph Theoretic Approaches for Analyzing Large-Scale Social Networks :

Computers

Author - Meghanathan, Natarajan
Publisher - IGI Global
Pages - 355
ISBN - 1522528156


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Detail - Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.

Apache Spark 2 for Beginners :

Computers

Author - Rajanarayanan Thottuvaikkatumana
Publisher - Packt Publishing Ltd
Pages - 332
ISBN - 178588669X


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Detail - Develop large-scale distributed data processing applications using Spark 2 in Scala and Python About This Book This book offers an easy introduction to the Spark framework published on the latest version of Apache Spark 2 Perform efficient data processing, machine learning and graph processing using various Spark components A practical guide aimed at beginners to get them up and running with Spark Who This Book Is For If you are an application developer, data scientist, or big data solutions architect who is interested in combining the data processing power of Spark from R, and consolidating data processing, stream processing, machine learning, and graph processing into one unified and highly interoperable framework with a uniform API using Scala or Python, this book is for you. What You Will Learn Get to know the fundamentals of Spark 2 and the Spark programming model using Scala and Python Know how to use Spark SQL and DataFrames using Scala and Python Get an introduction to Spark programming using R Perform Spark data processing, charting, and plotting using Python Get acquainted with Spark stream processing using Scala and Python Be introduced to machine learning using Spark MLlib Get started with graph processing using the Spark GraphX Bring together all that you've learned and develop a complete Spark application In Detail Spark is one of the most widely-used large-scale data processing engines and runs extremely fast. It is a framework that has tools that are equally useful for application developers as well as data scientists. This book starts with the fundamentals of Spark 2 and covers the core data processing framework and API, installation, and application development setup. Then the Spark programming model is introduced through real-world examples followed by Spark SQL programming with DataFrames. An introduction to SparkR is covered next. Later, we cover the charting and plotting features of Python in conjunction with Spark data processing. After that, we take a look at Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application. By the end of this book, you will have all the knowledge you need to develop efficient large-scale applications using Apache Spark. Style and approach Learn about Spark's infrastructure with this practical tutorial. With the help of real-world use cases on the main features of Spark we offer an easy introduction to the framework.

Apache Spark 2.x for Java Developers :

Computers

Author - Sourav Gulati, Sumit Kumar
Publisher - Packt Publishing Ltd
Pages - 350
ISBN - 178712942X


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Detail - 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.

Apache Spark for Java Developers :

Author - Sumit Kumar, Sourav Gulati
Publisher -
Pages - 370
ISBN - 9781787126497


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Detail - Unleash the data processing and analytics capability of Apache Spark with the language of choice-JavaAbout 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 the mainstream data processing by adding querying capability, machine learning, and graph processing using SparkWho This Book Is ForIf 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 Spark core Library* Perform analytics on data from various data sources such as Kafka, Flume, and Twitter using Spark Streaming Library* Learn SQL schema creation and analysis of structured data using various SQL functions including Windowing functions of Spark SQL Library* Explore the 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 SparkIn DetailApache 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 introduction to the Apache Spark ecosystem, followed by explaining the Spark installation and configuration, 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 using the various Java packages.By the end of the book, you will have a solid foundation in implementing the components in the Spark framework in Java to build fast, real-time applications

Scala and Spark for Big Data Analytics : Explore the concepts of functional programming, data streaming, and machine learning

Computers

Author - Md. Rezaul Karim, Sridhar Alla
Publisher - Packt Publishing Ltd
Pages - 786
ISBN - 1783550503


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Detail - Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

High Performance Spark : Best Practices for Scaling and Optimizing Apache Spark

Computers

Author - Holden Karau, Rachel Warren
Publisher - "O'Reilly Media, Inc."
Pages - 358
ISBN - 1491943157


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Detail - 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

Learning Apache Spark 2 :

Computers

Author - Muhammad Asif Abbasi
Publisher - Packt Publishing Ltd
Pages - 356
ISBN - 1785889583


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Detail - 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.

Learning Hadoop 2 :

Computers

Author - Garry Turkington, Gabriele Modena
Publisher - Packt Publishing Ltd
Pages - 382
ISBN - 1783285524


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Detail - If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. You are expected to be familiar with the Unix/Linux command-line interface and have some experience with the Java programming language. Familiarity with Hadoop would be a plus.

Scala for Machine Learning :

Computers

Author - Patrick R. Nicolas
Publisher - Packt Publishing Ltd
Pages - 520
ISBN - 178355875X


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Detail - Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

Apache Oozie Essentials :

Computers

Author - Jagat Jasjit Singh
Publisher - Packt Publishing Ltd
Pages - 164
ISBN - 1785888463


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Detail - Unleash the power of Apache Oozie to create and manage your big data and machine learning pipelines in one go About This Book Teaches you everything you need to know to get started with Apache Oozie from scratch and manage your data pipelines effortlessly Learn to write data ingestion workflows with the help of real-life examples from the author's own personal experience Embed Spark jobs to run your machine learning models on top of Hadoop Who This Book Is For If you are an expert Hadoop user who wants to use Apache Oozie to handle workflows efficiently, this book is for you. This book will be handy to anyone who is familiar with the basics of Hadoop and wants to automate data and machine learning pipelines. What You Will Learn Install and configure Oozie from source code on your Hadoop cluster Dive into the world of Oozie with Java MapReduce jobs Schedule Hive ETL and data ingestion jobs Import data from a database through Sqoop jobs in HDFS Create and process data pipelines with Pig, hive scripts as per business requirements. Run machine learning Spark jobs on Hadoop Create quick Oozie jobs using Hue Make the most of Oozie's security capabilities by configuring Oozie's security In Detail As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities is booming exponentially. This calls for data management. Hadoop caters to this need. Oozie fulfils this necessity for a scheduler for a Hadoop job by acting as a cron to better analyze data. Apache Oozie Essentials starts off with the basics right from installing and configuring Oozie from source code on your Hadoop cluster to managing your complex clusters. You will learn how to create data ingestion and machine learning workflows. This book is sprinkled with the examples and exercises to help you take your big data learning to the next level. You will discover how to write workflows to run your MapReduce, Pig ,Hive, and Sqoop scripts and schedule them to run at a specific time or for a specific business requirement using a coordinator. This book has engaging real-life exercises and examples to get you in the thick of things. Lastly, you'll get a grip of how to embed Spark jobs, which can be used to run your machine learning models on Hadoop. By the end of the book, you will have a good knowledge of Apache Oozie. You will be capable of using Oozie to handle large Hadoop workflows and even improve the availability of your Hadoop environment. Style and approach This book is a hands-on guide that explains Oozie using real-world examples. Each chapter is blended beautifully with fundamental concepts sprinkled in-between case study solution algorithms and topped off with self-learning exercises.