Ibm Data Engine For Hadoop And Spark

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IBM Data Engine for Hadoop and Spark

Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Nicolas Joly,Reinaldo Tetsuo Katahira,IBM Redbooks
IBM Data Engine for Hadoop and Spark Book PDF

Author : Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Nicolas Joly,Reinaldo Tetsuo Katahira,IBM Redbooks
Publisher : IBM Redbooks
Page : 126 pages
File Size : 52,5 Mb
Release : 2016-08-24
Category : Computers
ISBN : 9780738441931

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IBM Data Engine for Hadoop and Spark by Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Nicolas Joly,Reinaldo Tetsuo Katahira,IBM Redbooks Pdf

This IBM® Redbooks® publication provides topics to help the technical community take advantage of the resilience, scalability, and performance of the IBM Power SystemsTM platform to implement or integrate an IBM Data Engine for Hadoop and Spark solution for analytics solutions to access, manage, and analyze data sets to improve business outcomes. This book documents topics to demonstrate and take advantage of the analytics strengths of the IBM POWER8® platform, the IBM analytics software portfolio, and selected third-party tools to help solve customer's data analytic workload requirements. This book describes how to plan, prepare, install, integrate, manage, and show how to use the IBM Data Engine for Hadoop and Spark solution to run analytic workloads on IBM POWER8. In addition, this publication delivers documentation to complement available IBM analytics solutions to help your data analytic needs. This publication strengthens the position of IBM analytics and big data solutions with a well-defined and documented deployment model within an IBM POWER8 virtualized environment so that customers have a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted at technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) that are responsible for delivering analytics solutions and support on IBM Power Systems.

IBM Data Engine for Hadoop and Spark

Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Niicolas Joly,Reinaldo Tetsuo Katahira
IBM Data Engine for Hadoop and Spark Book PDF

Author : Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Niicolas Joly,Reinaldo Tetsuo Katahira
Publisher : Unknown
Page : 122 pages
File Size : 49,9 Mb
Release : 2016
Category : Data mining
ISBN : OCLC:1112602809

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IBM Data Engine for Hadoop and Spark by Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Niicolas Joly,Reinaldo Tetsuo Katahira Pdf

This IBM® Redbooks® publication provides topics to help the technical community take advantage of the resilience, scalability, and performance of the IBM Power SystemsTM platform to implement or integrate an IBM Data Engine for Hadoop and Spark solution for analytics solutions to access, manage, and analyze data sets to improve business outcomes. This book documents topics to demonstrate and take advantage of the analytics strengths of the IBM POWER8® platform, the IBM analytics software portfolio, and selected third-party tools to help solve customer's data analytic workload requirements. This book describes how to plan, prepare, install, integrate, manage, and show how to use the IBM Data Engine for Hadoop and Spark solution to run analytic workloads on IBM POWER8. In addition, this publication delivers documentation to complement available IBM analytics solutions to help your data analytic needs. This publication strengthens the position of IBM analytics and big data solutions with a well-defined and documented deployment model within an IBM POWER8 virtualized environment so that customers have a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted at technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) that are responsible for delivering analytics solutions and support on IBM Power Systems.

Bridging Relational and NoSQL Databases

Gaspar, Drazena,Coric, Ivica
Bridging Relational and NoSQL Databases Book PDF

Author : Gaspar, Drazena,Coric, Ivica
Publisher : IGI Global
Page : 338 pages
File Size : 53,7 Mb
Release : 2017-11-30
Category : Computers
ISBN : 9781522533863

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Bridging Relational and NoSQL Databases by Gaspar, Drazena,Coric, Ivica Pdf

Relational databases have been predominant for many years and are used throughout various industries. The current system faces challenges related to size and variety of data thus the NoSQL databases emerged. By joining these two database models, there is room for crucial developments in the field of computer science. Bridging Relational and NoSQL Databases is an innovative source of academic content on the convergence process between databases and describes key features of the next database generation. Featuring coverage on a wide variety of topics and perspectives such as BASE approach, CAP theorem, and hybrid and native solutions, this publication is ideally designed for professionals and researchers interested in the features and collaboration of relational and NoSQL databases.

Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers

Scott Vetter,Helen Lu,Maciej Olejniczak,IBM Redbooks
Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers Book PDF

Author : Scott Vetter,Helen Lu,Maciej Olejniczak,IBM Redbooks
Publisher : IBM Redbooks
Page : 82 pages
File Size : 45,7 Mb
Release : 2018-01-31
Category : Computers
ISBN : 9780738456607

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Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers by Scott Vetter,Helen Lu,Maciej Olejniczak,IBM Redbooks Pdf

Data warehouses were developed for many good reasons, such as providing quick query and reporting for business operations, and business performance. However, over the years, due to the explosion of applications and data volume, many existing data warehouses have become difficult to manage. Extract, Transform, and Load (ETL) processes are taking longer, missing their allocated batch windows. In addition, data types that are required for business analysis have expanded from structured data to unstructured data. The Apache open source Hadoop platform provides a great alternative for solving these problems. IBM® has committed to open source since the early years of open Linux. IBM and Hortonworks together are committed to Apache open source software more than any other company. IBM Power SystemsTM servers are built with open technologies and are designed for mission-critical data applications. Power Systems servers use technology from the OpenPOWER Foundation, an open technology infrastructure that uses the IBM POWER® architecture to help meet the evolving needs of big data applications. The combination of Power Systems with Hortonworks Data Platform (HDP) provides users with a highly efficient platform that provides leadership performance for big data workloads such as Hadoop and Spark. This IBM RedpaperTM publication provides details about Enterprise Data Warehouse (EDW) optimization with Hadoop on Power Systems. Many people know Power Systems from the IBM AIX® platform, but might not be familiar with IBM PowerLinuxTM, so part of this paper provides a Power Systems overview. A quick introduction to Hadoop is provided for those not familiar with the topic. Details of HDP on Power Reference architecture are included that will help both software architects and infrastructure architects understand the design. In the optimization chapter, we describe various topics: traditional EDW offload, sizing guidelines, performance tuning, IBM Elastic StorageTM Server (ESS) for data-intensive workload, IBM Big SQL as the common structured query language (SQL) engine for Hadoop platform, and tools that are available on Power Systems that are related to EDW optimization. We also dedicate some pages to the analytics components (IBM Data Science Experience (IBM DSX) and IBM SpectrumTM Conductor for Spark workload) for the Hadoop infrastructure.

IBM Power Systems L and LC Server Positioning Guide

Scott Vetter,Tonny Bastiaans,Andrew Laidlaw,IBM Redbooks
IBM Power Systems L and LC Server Positioning Guide Book PDF

Author : Scott Vetter,Tonny Bastiaans,Andrew Laidlaw,IBM Redbooks
Publisher : IBM Redbooks
Page : 30 pages
File Size : 54,9 Mb
Release : 2017-02-16
Category : Computers
ISBN : 9780738455815

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IBM Power Systems L and LC Server Positioning Guide by Scott Vetter,Tonny Bastiaans,Andrew Laidlaw,IBM Redbooks Pdf

This IBM® RedpaperTM publication is written to assist you in locating the optimal server/workload fit within the IBM Power SystemsTM L and IBM OpenPOWER LC product lines. IBM has announced several scale-out servers, and as a partner in the OpenPOWER organization, unique design characteristics that are engineered into the LC line have broadened the suite of available workloads beyond typical client OS hosting. This paper looks at the benefits of the Power Systems L servers and OpenPOWER LC servers, and how they are different, providing unique benefits for Enterprise workloads and use cases.

Apache Spark Implementation on IBM z/OS

Lydia Parziale,Joe Bostian,Ravi Kumar,Ulrich Seelbach,Zhong Yu Ye,IBM Redbooks
Apache Spark Implementation on IBM z OS Book PDF

Author : Lydia Parziale,Joe Bostian,Ravi Kumar,Ulrich Seelbach,Zhong Yu Ye,IBM Redbooks
Publisher : IBM Redbooks
Page : 142 pages
File Size : 45,9 Mb
Release : 2016-08-13
Category : Computers
ISBN : 9780738414966

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Apache Spark Implementation on IBM z/OS by Lydia Parziale,Joe Bostian,Ravi Kumar,Ulrich Seelbach,Zhong Yu Ye,IBM Redbooks Pdf

The term big data refers to extremely large sets of data that are analyzed to reveal insights, such as patterns, trends, and associations. The algorithms that analyze this data to provide these insights must extract value from a wide range of data sources, including business data and live, streaming, social media data. However, the real value of these insights comes from their timeliness. Rapid delivery of insights enables anyone (not only data scientists) to make effective decisions, applying deep intelligence to every enterprise application. Apache Spark is an integrated analytics framework and runtime to accelerate and simplify algorithm development, depoyment, and realization of business insight from analytics. Apache Spark on IBM® z/OS® puts the open source engine, augmented with unique differentiated features, built specifically for data science, where big data resides. This IBM Redbooks® publication describes the installation and configuration of IBM z/OS Platform for Apache Spark for field teams and clients. Additionally, it includes examples of business analytics scenarios.

Apache Spark for the Enterprise: Setting the Business Free

Oliver Draese,Eberhard Hechler,Hong Min,Catherine Moxey,Pallavi Priyadarshini,Mark Simmonds,Mythili Venkatakrishnan,George Wang,IBM Redbooks
Apache Spark for the Enterprise Setting the Business Free Book PDF

Author : Oliver Draese,Eberhard Hechler,Hong Min,Catherine Moxey,Pallavi Priyadarshini,Mark Simmonds,Mythili Venkatakrishnan,George Wang,IBM Redbooks
Publisher : IBM Redbooks
Page : 56 pages
File Size : 42,7 Mb
Release : 2016-02-09
Category : Computers
ISBN : 9780738455044

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Apache Spark for the Enterprise: Setting the Business Free by Oliver Draese,Eberhard Hechler,Hong Min,Catherine Moxey,Pallavi Priyadarshini,Mark Simmonds,Mythili Venkatakrishnan,George Wang,IBM Redbooks Pdf

Analytics is increasingly an integral part of day-to-day operations at today's leading businesses, and transformation is also occurring through huge growth in mobile and digital channels. Enterprise organizations are attempting to leverage analytics in new ways and transition existing analytics capabilities to respond with more flexibility while making the most efficient use of highly valuable data science skills. The recent growth and adoption of Apache Spark as an analytics framework and platform is very timely and helps meet these challenging demands. The Apache Spark environment on IBM z/OS® and Linux on IBM z SystemsTM platforms allows this analytics framework to run on the same enterprise platform as the originating sources of data and transactions that feed it. If most of the data that will be used for Apache Spark analytics, or the most sensitive or quickly changing data is originating on z/OS, then an Apache Spark z/OS based environment will be the optimal choice for performance, security, and governance. This IBM® RedpaperTM publication explores the enterprise analytics market, use of Apache Spark on IBM z SystemsTM platforms, integration between Apache Spark and other enterprise data sources, and case studies and examples of what can be achieved with Apache Spark in enterprise environments. It is of interest to data scientists, data engineers, enterprise architects, or anybody looking to better understand how to combine an analytics framework and platform on enterprise systems.

IBM Software Defined Infrastructure for Big Data Analytics Workloads

Dino Quintero,Daniel de Souza Casali,Marcelo Correia Lima,Istvan Gabor Szabo,Maciej Olejniczak,Tiago Rodrigues de Mello,Nilton Carlos dos Santos,IBM Redbooks
IBM Software Defined Infrastructure for Big Data Analytics Workloads Book PDF

Author : Dino Quintero,Daniel de Souza Casali,Marcelo Correia Lima,Istvan Gabor Szabo,Maciej Olejniczak,Tiago Rodrigues de Mello,Nilton Carlos dos Santos,IBM Redbooks
Publisher : IBM Redbooks
Page : 180 pages
File Size : 41,7 Mb
Release : 2015-06-29
Category : Computers
ISBN : 9780738440774

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IBM Software Defined Infrastructure for Big Data Analytics Workloads by Dino Quintero,Daniel de Souza Casali,Marcelo Correia Lima,Istvan Gabor Szabo,Maciej Olejniczak,Tiago Rodrigues de Mello,Nilton Carlos dos Santos,IBM Redbooks Pdf

This IBM® Redbooks® publication documents how IBM Platform Computing, with its IBM Platform Symphony® MapReduce framework, IBM Spectrum Scale (based Upon IBM GPFSTM), IBM Platform LSF®, the Advanced Service Controller for Platform Symphony are work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offeringsm such as Apach Spark, Storm, MongoDB, Cassandra, and so on. It describes the different ways to run Hadoop in a big data environment, and demonstrates how IBM Platform Computing solutions, such as Platform Symphony and Platform LSF with its MapReduce Accelerator, can help performance and agility to run Hadoop on distributed workload managers offered by IBM. This information is for technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for delivering cost-effective cloud services and big data solutions on IBM Power SystemsTM to help uncover insights among client's data so they can optimize product development and business results.

Data Analytics for Pandemics

Gitanjali Rahul Shinde,Asmita Balasaheb Kalamkar,Parikshit N. Mahalle,Nilanjan Dey
Data Analytics for Pandemics Book PDF

Author : Gitanjali Rahul Shinde,Asmita Balasaheb Kalamkar,Parikshit N. Mahalle,Nilanjan Dey
Publisher : CRC Press
Page : 73 pages
File Size : 50,7 Mb
Release : 2020-08-30
Category : Computers
ISBN : 9781000204452

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Data Analytics for Pandemics by Gitanjali Rahul Shinde,Asmita Balasaheb Kalamkar,Parikshit N. Mahalle,Nilanjan Dey Pdf

Epidemic trend analysis, timeline progression, prediction, and recommendation are critical for initiating effective public health control strategies, and AI and data analytics play an important role in epidemiology, diagnostic, and clinical fronts. The focus of this book is data analytics for COVID-19, which includes an overview of COVID-19 in terms of epidemic/pandemic, data processing and knowledge extraction. Data sources, storage and platforms are discussed along with discussions on data models, their performance, different big data techniques, tools and technologies. This book also addresses the challenges in applying analytics to pandemic scenarios, case studies and control strategies. Aimed at Data Analysts, Epidemiologists and associated researchers, this book: discusses challenges of AI model for big data analytics in pandemic scenarios; explains how different big data analytics techniques can be implemented; provides a set of recommendations to minimize infection rate of COVID-19; summarizes various techniques of data processing and knowledge extraction; enables users to understand big data analytics techniques required for prediction purposes.

AI and Big Data on IBM Power Systems Servers

Scott Vetter,Ivaylo B. Bozhinov,Anto A John,Rafael Freitas de Lima,Ahmed.(Mash) Mashhour,James Van Oosten,Fernando Vermelho,Allison White,IBM Redbooks
AI and Big Data on IBM Power Systems Servers Book PDF

Author : Scott Vetter,Ivaylo B. Bozhinov,Anto A John,Rafael Freitas de Lima,Ahmed.(Mash) Mashhour,James Van Oosten,Fernando Vermelho,Allison White,IBM Redbooks
Publisher : IBM Redbooks
Page : 162 pages
File Size : 48,6 Mb
Release : 2019-04-10
Category : Computers
ISBN : 9780738457512

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AI and Big Data on IBM Power Systems Servers by Scott Vetter,Ivaylo B. Bozhinov,Anto A John,Rafael Freitas de Lima,Ahmed.(Mash) Mashhour,James Van Oosten,Fernando Vermelho,Allison White,IBM Redbooks Pdf

As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power SystemsTM IBM SpectrumTM Scale IBM Data Science Experience (IBM DSX) IBM Elastic StorageTM Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale

Wei Gong,Linda Cham,Prashanth Shetty,John Sing,IBM Redbooks
Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale Book PDF

Author : Wei Gong,Linda Cham,Prashanth Shetty,John Sing,IBM Redbooks
Publisher : IBM Redbooks
Page : 42 pages
File Size : 41,7 Mb
Release : 2021-08-27
Category : Computers
ISBN : 9780738459387

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Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale by Wei Gong,Linda Cham,Prashanth Shetty,John Sing,IBM Redbooks Pdf

This IBM® Redpaper publication provides guidance on building an enterprise-grade data lake by using IBM Spectrum® Scale and Cloudera Data Platform (CDP) Private Cloud Base for performing in-place Cloudera Hadoop or Cloudera Spark-based analytics. It also covers the benefits of the integrated solution and gives guidance about the types of deployment models and considerations during the implementation of these models. August 2021 update added CES protocol support in Hadoop environment

Harness the Power of Big Data The IBM Big Data Platform

Paul Zikopoulos,Dirk deRoos,Krishnan Parasuraman,Thomas Deutsch,James Giles,David Corrigan
Harness the Power of Big Data The IBM Big Data Platform Book PDF

Author : Paul Zikopoulos,Dirk deRoos,Krishnan Parasuraman,Thomas Deutsch,James Giles,David Corrigan
Publisher : McGraw Hill Professional
Page : 281 pages
File Size : 54,9 Mb
Release : 2012-11-08
Category : Computers
ISBN : 9780071808187

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Harness the Power of Big Data The IBM Big Data Platform by Paul Zikopoulos,Dirk deRoos,Krishnan Parasuraman,Thomas Deutsch,James Giles,David Corrigan Pdf

Boost your Big Data IQ! Gain insight into how to govern and consume IBM’s unique in-motion and at-rest Big Data analytic capabilities Big Data represents a new era of computing—an inflection point of opportunity where data in any format may be explored and utilized for breakthrough insights—whether that data is in-place, in-motion, or at-rest. IBM is uniquely positioned to help clients navigate this transformation. This book reveals how IBM is infusing open source Big Data technologies with IBM innovation that manifest in a platform capable of "changing the game." The four defining characteristics of Big Data—volume, variety, velocity, and veracity—are discussed. You’ll understand how IBM is fully committed to Hadoop and integrating it into the enterprise. Hear about how organizations are taking inventories of their existing Big Data assets, with search capabilities that help organizations discover what they could already know, and extend their reach into new data territories for unprecedented model accuracy and discovery. In this book you will also learn not just about the technologies that make up the IBM Big Data platform, but when to leverage its purpose-built engines for analytics on data in-motion and data at-rest. And you’ll gain an understanding of how and when to govern Big Data, and how IBM’s industry-leading InfoSphere integration and governance portfolio helps you understand, govern, and effectively utilize Big Data. Industry use cases are also included in this practical guide.

Big Data Management and Processing

Kuan-Ching Li,Hai Jiang,Albert Y. Zomaya
Big Data Management and Processing Book PDF

Author : Kuan-Ching Li,Hai Jiang,Albert Y. Zomaya
Publisher : CRC Press
Page : 489 pages
File Size : 45,9 Mb
Release : 2017-05-19
Category : Business & Economics
ISBN : 9781498768085

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Big Data Management and Processing by Kuan-Ching Li,Hai Jiang,Albert Y. Zomaya Pdf

From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

IBM Reference Architecture for Genomics, Power Systems Edition

Dino Quintero,Luis Bolinches,Marcelo Correia Lima,Katarzyna Pasierb,William dos Santos,IBM Redbooks
IBM Reference Architecture for Genomics Power Systems Edition Book PDF

Author : Dino Quintero,Luis Bolinches,Marcelo Correia Lima,Katarzyna Pasierb,William dos Santos,IBM Redbooks
Publisher : IBM Redbooks
Page : 140 pages
File Size : 40,7 Mb
Release : 2016-04-05
Category : Computers
ISBN : 9780738441634

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IBM Reference Architecture for Genomics, Power Systems Edition by Dino Quintero,Luis Bolinches,Marcelo Correia Lima,Katarzyna Pasierb,William dos Santos,IBM Redbooks Pdf

This IBM® Redbooks® publication introduces the IBM Reference Architecture for Genomics, IBM Power SystemsTM edition on IBM POWER8®. It addresses topics such as why you would implement Life Sciences workloads on IBM POWER8, and shows how to use such solution to run Life Sciences workloads using IBM PlatformTM Computing software to help set up the workloads. It also provides technical content to introduce the IBM POWER8 clustered solution for Life Sciences workloads. This book customizes and tests Life Sciences workloads with a combination of an IBM Platform Computing software solution stack, Open Stack, and third party applications. All of these applications use IBM POWER8, and IBM Spectrum ScaleTM for a high performance file system. This book helps strengthen IBM Life Sciences solutions on IBM POWER8 with a well-defined and documented deployment model within an IBM Platform Computing and an IBM POWER8 clustered environment. This system provides clients in need of a modular, cost-effective, and robust solution with a planned foundation for future growth. This book highlights IBM POWER8 as a flexible infrastructure for clients looking to deploy life sciences workloads, and at the same time reduce capital expenditures, operational expenditures, and optimization of resources. This book helps answer clients' workload challenges in particular with Life Sciences applications, and provides expert-level documentation and how-to-skills to worldwide teams that provide Life Sciences solutions and support to give a broad understanding of a new architecture.

Mastering Apache Spark 2.x

Romeo Kienzler
Mastering Apache Spark 2 x Book PDF

Author : Romeo Kienzler
Publisher : Packt Publishing Ltd
Page : 354 pages
File Size : 50,9 Mb
Release : 2017-07-26
Category : Computers
ISBN : 9781785285226

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Mastering Apache Spark 2.x by Romeo Kienzler Pdf

Advanced analytics on your Big Data with latest Apache Spark 2.x About This Book An advanced guide with a combination of instructions and practical examples to extend the most up-to date Spark functionalities. Extend your data processing capabilities to process huge chunk of data in minimum time using advanced concepts in Spark. Master the art of real-time processing with the help of Apache Spark 2.x 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 Examine Advanced Machine Learning and DeepLearning with MLlib, SparkML, SystemML, H2O and DeepLearning4J Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming Evaluate large-scale Graph Processing and Analysis using GraphX and GraphFrames Apply Apache Spark in Elastic deployments using Jupyter and Zeppelin Notebooks, Docker, Kubernetes and the IBM Cloud Understand internal details of cost based optimizers used in Catalyst, SystemML and GraphFrames Learn how specific parameter settings affect overall performance of an Apache Spark cluster Leverage Scala, R and python for your data science projects In Detail Apache Spark is an in-memory cluster-based parallel processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and SQL. This book aims to take your knowledge of Spark to the next level by teaching you how to expand Spark's functionality and implement your data flows and machine/deep learning programs on top of the platform. The book commences with an overview of the Spark ecosystem. It will introduce you to Project Tungsten and Catalyst, two of the major advancements of Apache Spark 2.x. You will understand how memory management and binary processing, cache-aware computation, and code generation are used to speed things up dramatically. The book extends to show how to incorporate H20, SystemML, and Deeplearning4j for machine learning, and Jupyter Notebooks and Kubernetes/Docker for cloud-based Spark. During the course of the book, you will learn about the latest enhancements to Apache Spark 2.x, such as interactive querying of live data and unifying DataFrames and Datasets. You will also learn about the updates on the APIs and how DataFrames and Datasets affect SQL, machine learning, graph processing, and streaming. You will learn to use Spark as a big data operating system, understand how to implement advanced analytics on the new APIs, and explore how easy it is to use Spark in day-to-day tasks. 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.