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IBM Data Engine for Hadoop and Spark

IBM Data Engine for Hadoop and Spark
  • Author : Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Nicolas Joly,Reinaldo Tetsuo Katahira,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2016-08-24
  • Total pages :122
  • ISBN : 9780738441931
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Summary : 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

IBM Data Engine for Hadoop and Spark
  • Author : Dino Quintero,Luis Bolinches,Aditya Gandakusuma Sutandyo,Niicolas Joly (IBM pre-sales architect),Reinaldo Tetsuo Katahira
  • Publisher :Unknown
  • Release Date :2016
  • Total pages :229
  • ISBN : OCLC:958459723
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Summary :

Bridging Relational and NoSQL Databases

Bridging Relational and NoSQL Databases
  • Author : Gaspar, Drazena,Coric, Ivica
  • Publisher :Unknown
  • Release Date :2017-11-30
  • Total pages :338
  • ISBN : 9781522533863
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Summary : 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.

IBM Power Systems L and LC Server Positioning Guide

IBM Power Systems L and LC Server Positioning Guide
  • Author : Scott Vetter,Tonny Bastiaans,Andrew Laidlaw,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2017-02-16
  • Total pages :32
  • ISBN : 9780738455815
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Summary : 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.

Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers

Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers
  • Author : Scott Vetter,Helen Lu,Maciej Olejniczak,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2018-01-31
  • Total pages :82
  • ISBN : 9780738456607
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Summary : 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 Platform Computing Solutions for High Performance and Technical Computing Workloads

IBM Platform Computing Solutions for High Performance and Technical Computing Workloads
  • 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 :Unknown
  • Release Date :2015-06-19
  • Total pages :176
  • ISBN : 9780738440750
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Summary : This IBM® Redbooks® publication is a refresh of IBM Technical Computing Clouds, SG24-8144, Enhance Inbound and Outbound Marketing with a Trusted Single View of the Customer, SG24-8173, and IBM Platform Computing Integration Solutions, SG24-8081, with a focus on High Performance and Technical Computing on IBM Power SystemsTM. This book describes synergies across the IBM product portfolio by using case scenarios and showing solutions such as IBM SpectrumTM Scale (formerly GPFSTM). This book also reflects and documents the IBM Platform Computing Cloud Services as part of IBM Platform Symphony® for analytics workloads and IBM Platform LSF® (with new features, such as a Hadoop connector, a MapReduce accelerator, and dynamic cluster) for job scheduling. Both products are used to help customers schedule and analyze large amounts of data for business productivity and competitive advantages. This book is targeted at technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) that are responsible for delivering cost-effective cloud services and big data solutions on IBM Power Systems to uncover insights among client data so that they can take actions to optimize business results, product development, and scientific discoveries.

AI and Big Data on IBM Power Systems Servers

AI and Big Data on IBM Power Systems Servers
  • 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 :Unknown
  • Release Date :2019-04-10
  • Total pages :162
  • ISBN : 9780738457512
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Summary : 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.

IBM Software Defined Infrastructure for Big Data Analytics Workloads

IBM Software Defined Infrastructure for Big Data Analytics Workloads
  • 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 :Unknown
  • Release Date :2015-06-29
  • Total pages :178
  • ISBN : 9780738440774
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Summary : 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.

Hortonworks Data Platform with IBM Spectrum Scale: Reference Guide for Building an Integrated Solution

Hortonworks Data Platform with IBM Spectrum Scale: Reference Guide for Building an Integrated Solution
  • Author : Sandeep R. Patil,Wei G. Gong,Pallavi Galgali,Piyush Chaudhary,Muthu Muthiah,Yong ZY Zheng,Larry Coyne,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2018-06-26
  • Total pages :30
  • ISBN : 9780738456966
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Summary : This IBM® RedpaperTM publication provides guidance on building an enterprise-grade data lake by using IBM SpectrumTM Scale and Hortonworks Data Platform for performing in-place Hadoop or Spark-based analytics. It covers the benefits of the integrated solution, and gives guidance about the types of deployment models and considerations during the implementation of these models. Hortonworks Data Platform (HDP) is a leading Hadoop and Spark distribution. HDP addresses the complete needs of data-at-rest, powers real-time customer applications, and delivers robust analytics that accelerate decision making and innovation. IBM Spectrum ScaleTM is flexible and scalable software-defined file storage for analytics workloads. Enterprises around the globe have deployed IBM Spectrum Scale to form large data lakes and content repositories to perform high-performance computing (HPC) and analytics workloads. It can scale performance and capacity both without bottlenecks.

Implementing an Optimized Analytics Solution on IBM Power Systems

Implementing an Optimized Analytics Solution on IBM Power Systems
  • Author : Dino Quintero,Kanako Harada,Reinaldo Tetsuo Katahira,Antonio Moreira de Oliveira Neto,Robert Simon,Brian Yaeger,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2016-06-01
  • Total pages :296
  • ISBN : 9780738441689
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Summary : This IBM® Redbooks® publication addresses topics to use the virtualization strengths of the IBM POWER8® platform to solve clients' system resource utilization challenges and maximize systems' throughput and capacity. This book addresses performance tuning topics that will help answer clients' complex analytic workload requirements, help maximize systems' resources, and provide expert-level documentation to transfer the how-to-skills to the worldwide teams. This book strengthens the position of IBM Analytics and Big Data solutions with a well-defined and documented deployment model within a POWER8 virtualized environment, offering clients a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted toward technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing analytics solutions and support on IBM Power SystemsTM.

Apache Spark Implementation on IBM z/OS

Apache Spark Implementation on IBM z/OS
  • Author : Lydia Parziale,Joe Bostian,Ravi Kumar,Ulrich Seelbach,Zhong Yu Ye,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2016-08-13
  • Total pages :142
  • ISBN : 9780738414966
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Summary : 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.

Spark in Action, Second Edition

Spark in Action, Second Edition
  • Author : Jean-Georges Perrin
  • Publisher :Unknown
  • Release Date :2020-06-02
  • Total pages :576
  • ISBN : 9781617295522
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Summary : Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment

Learning Spark

Learning Spark
  • Author : Mark Hamstra,Holden Karau,Matei Zaharia,Andy Konwinski,Patrick Wendell
  • Publisher :Unknown
  • Release Date :2015-02-22
  • Total pages :276
  • ISBN : 1449358624
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Summary : This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. 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.

Building Big Data and Analytics Solutions in the Cloud

Building Big Data and Analytics Solutions in the Cloud
  • Author : Wei-Dong Zhu,Manav Gupta,Ven Kumar,Sujatha Perepa,Arvind Sathi,Craig Statchuk,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2014-12-08
  • Total pages :101
  • ISBN : 9780738453996
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Summary : Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.

IBM Spectrum Scale: Big Data and Analytics Solution Brief

IBM Spectrum Scale: Big Data and Analytics Solution Brief
  • Author : Wei G. Gong,Sandeep R. Patil,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2018-01-23
  • Total pages :14
  • ISBN : 9780738456638
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Summary : This IBM® RedguideTM publication describes big data and analytics deployments that are built on IBM Spectrum ScaleTM. IBM Spectrum Scale is a proven enterprise-level distributed file system that is a high-performance and cost-effective alternative to Hadoop Distributed File System (HDFS) for Hadoop analytics services. IBM Spectrum Scale includes NFS, SMB, and Object services and meets the performance that is required by many industry workloads, such as technical computing, big data, analytics, and content management. IBM Spectrum Scale provides world-class, web-based storage management with extreme scalability, flash accelerated performance, and automatic policy-based storage tiering from flash through disk to the cloud, which reduces storage costs up to 90% while improving security and management efficiency in cloud, big data, and analytics environments. This Redguide publication is intended for technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing Hadoop analytics services and are interested in learning about the benefits of the use of IBM Spectrum Scale as an alternative to HDFS.

Turning Data into Insight with IBM Machine Learning for z/OS

Turning Data into Insight with IBM Machine Learning for z/OS
  • Author : Samantha Buhler,Guanjun Cai,John Goodyear,Edrian Irizarry,Nora Kissari,Zhuo Ling,Nicholas Marion,Aleksandr Petrov,Junfei Shen,Wanting Wang,He Sheng Yang,Dai Yi,Xavier Yuen,Hao Zhang,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2018-09-11
  • Total pages :180
  • ISBN : 9780738457130
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Summary : The exponential growth in data over the last decade coupled with a drastic drop in cost of storage has enabled organizations to amass a large amount of data. This vast data becomes the new natural resource that these organizations must tap in to innovate and stay ahead of the competition, and they must do so in a secure environment that protects the data throughout its lifecyle and data access in real time at any time. When it comes to security, nothing can rival IBM® Z, the multi-workload transactional platform that powers the core business processes of the majority of the Fortune 500 enterprises with unmatched security, availability, reliability, and scalability. With core transactions and data originating on IBM Z, it simply makes sense for analytics to exist and run on the same platform. For years, some businesses chose to move their sensitive data off IBM Z to platforms that include data lakes, Hadoop, and warehouses for analytics processing. However, the massive growth of digital data, the punishing cost of security exposures as well as the unprecedented demand for instant actionable intelligence from data in real time have convinced them to rethink that decision and, instead, embrace the strategy of data gravity for analytics. At the core of data gravity is the conviction that analytics must exist and run where the data resides. An IBM client eloquently compares this change in analytics strategy to a shift from "moving the ocean to the boat to moving the boat to the ocean," where the boat is the analytics and the ocean is the data. IBM respects and invests heavily on data gravity because it recognizes the tremendous benefits that data gravity can deliver to you, including reduced cost and minimized security risks. IBM Machine Learning for z/OS® is one of the offerings that decidedly move analytics to Z where your mission-critical data resides. In the inherently secure Z environment, your machine learning scoring services can co-exist with your transactional applications and data, supporting high throughput and minimizing response time while delivering consistent service level agreements (SLAs). This book introduces Machine Learning for z/OS version 1.1.0 and describes its unique value proposition. It provides step-by-step guidance for you to get started with the program, including best practices for capacity planning, installation and configuration, administration and operation. Through a retail example, the book shows how you can use the versatile and intuitive web user interface to quickly train, build, evaluate, and deploy a model. Most importantly, it examines use cases across industries to illustrate how you can easily turn your massive data into valuable insights with Machine Learning for z/OS.

The Enterprise Big Data Lake

The Enterprise Big Data Lake
  • Author : Alex Gorelik
  • Publisher :Unknown
  • Release Date :2019-02-21
  • Total pages :224
  • ISBN : 9781491931509
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Summary : The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries

Big Data Processing with Apache Spark

Big Data Processing with Apache Spark
  • Author : Srini Penchikala
  • Publisher :Unknown
  • Release Date :2021
  • Total pages :229
  • ISBN : 9781387659951
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Summary :

Apache Spark Implementation on IBM z/OS

Apache Spark Implementation on IBM z/OS
  • Author : Lydia Parziale,Joe Bostian,Ravi Kumar,Ulrich Seelbach,Zhong Yu Ye,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2016-08-13
  • Total pages :142
  • ISBN : 9780738414966
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Summary : 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.

Hadoop For Dummies

Hadoop For Dummies
  • Author : Dirk deRoos
  • Publisher :Unknown
  • Release Date :2014-04-14
  • Total pages :416
  • ISBN : 9781118607558
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Summary : Let Hadoop For Dummies help harness the power of your data and rein in the information overload Big data has become big business, and companies and organizations of all sizes are struggling to find ways to retrieve valuable information from their massive data sets with becoming overwhelmed. Enter Hadoop and this easy-to-understand For Dummies guide. Hadoop For Dummies helps readers understand the value of big data, make a business case for using Hadoop, navigate the Hadoop ecosystem, and build and manage Hadoop applications and clusters. Explains the origins of Hadoop, its economic benefits, and its functionality and practical applications Helps you find your way around the Hadoop ecosystem, program MapReduce, utilize design patterns, and get your Hadoop cluster up and running quickly and easily Details how to use Hadoop applications for data mining, web analytics and personalization, large-scale text processing, data science, and problem-solving Shows you how to improve the value of your Hadoop cluster, maximize your investment in Hadoop, and avoid common pitfalls when building your Hadoop cluster From programmers challenged with building and maintaining affordable, scaleable data systems to administrators who must deal with huge volumes of information effectively and efficiently, this how-to has something to help you with Hadoop.

Data Algorithms

Data Algorithms
  • Author : Mahmoud Parsian
  • Publisher :Unknown
  • Release Date :2015-07-13
  • Total pages :778
  • ISBN : 9781491906156
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Summary : If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)