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

Author: Sandeep R. Patil

Publisher: IBM Redbooks

Published: 2018-06-26

Total Pages: 30

ISBN-13: 0738456969

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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.

Book Synopsis Hortonworks Data Platform with IBM Spectrum Scale: Reference Guide for Building an Integrated Solution by : Sandeep R. Patil

Download or read book Hortonworks Data Platform with IBM Spectrum Scale: Reference Guide for Building an Integrated Solution written by Sandeep R. Patil and published by IBM Redbooks. This book was released on 2018-06-26 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hortonworks Data Platform with IBM Spectrum Scale

Author: Sandeep Patil

Publisher:

Published: 2018

Total Pages: 30

ISBN-13:

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Book Synopsis Hortonworks Data Platform with IBM Spectrum Scale by : Sandeep Patil

Download or read book Hortonworks Data Platform with IBM Spectrum Scale written by Sandeep Patil and published by . This book was released on 2018 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

IBM Spectrum Scale: Big Data and Analytics Solution Brief

Author: Wei G. Gong

Publisher: IBM Redbooks

Published: 2018-01-23

Total Pages: 14

ISBN-13: 0738456632

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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.

Book Synopsis IBM Spectrum Scale: Big Data and Analytics Solution Brief by : Wei G. Gong

Download or read book IBM Spectrum Scale: Big Data and Analytics Solution Brief written by Wei G. Gong and published by IBM Redbooks. This book was released on 2018-01-23 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale

Author: Wei Gong

Publisher: IBM Redbooks

Published: 2021-08-27

Total Pages: 42

ISBN-13: 0738459380

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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

Book Synopsis Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale by : Wei Gong

Download or read book Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale written by Wei Gong and published by IBM Redbooks. This book was released on 2021-08-27 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

IBM Spectrum Scale Security

Author: Felipe Knop

Publisher: IBM Redbooks

Published: 2018-09-18

Total Pages: 116

ISBN-13: 0738457167

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Storage systems must provide reliable and convenient data access to all authorized users while simultaneously preventing threats coming from outside or even inside the enterprise. Security threats come in many forms, from unauthorized access to data, data tampering, denial of service, and obtaining privileged access to systems. According to the Storage Network Industry Association (SNIA), data security in the context of storage systems is responsible for safeguarding the data against theft, prevention of unauthorized disclosure of data, prevention of data tampering, and accidental corruption. This process ensures accountability, authenticity, business continuity, and regulatory compliance. Security for storage systems can be classified as follows: Data storage (data at rest, which includes data durability and immutability) Access to data Movement of data (data in flight) Management of data IBM® Spectrum Scale is a software-defined storage system for high performance, large-scale workloads on-premises or in the cloud. IBM SpectrumTM Scale addresses all four aspects of security by securing data at rest (protecting data at rest with snapshots, and backups and immutability features) and securing data in flight (providing secure management of data, and secure access to data by using authentication and authorization across multiple supported access protocols). These protocols include POSIX, NFS, SMB, Hadoop, and Object (REST). For automated data management, it is equipped with powerful information lifecycle management (ILM) tools that can help administer unstructured data by providing the correct security for the correct data. This IBM RedpaperTM publication details the various aspects of security in IBM Spectrum ScaleTM, including the following items: Security of data in transit Security of data at rest Authentication Authorization Hadoop security Immutability Secure administration Audit logging Security for transparent cloud tiering (TCT) Security for OpenStack drivers Unless stated otherwise, the functions that are mentioned in this paper are available in IBM Spectrum Scale V4.2.1 or later releases.

Book Synopsis IBM Spectrum Scale Security by : Felipe Knop

Download or read book IBM Spectrum Scale Security written by Felipe Knop and published by IBM Redbooks. This book was released on 2018-09-18 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Storage systems must provide reliable and convenient data access to all authorized users while simultaneously preventing threats coming from outside or even inside the enterprise. Security threats come in many forms, from unauthorized access to data, data tampering, denial of service, and obtaining privileged access to systems. According to the Storage Network Industry Association (SNIA), data security in the context of storage systems is responsible for safeguarding the data against theft, prevention of unauthorized disclosure of data, prevention of data tampering, and accidental corruption. This process ensures accountability, authenticity, business continuity, and regulatory compliance. Security for storage systems can be classified as follows: Data storage (data at rest, which includes data durability and immutability) Access to data Movement of data (data in flight) Management of data IBM® Spectrum Scale is a software-defined storage system for high performance, large-scale workloads on-premises or in the cloud. IBM SpectrumTM Scale addresses all four aspects of security by securing data at rest (protecting data at rest with snapshots, and backups and immutability features) and securing data in flight (providing secure management of data, and secure access to data by using authentication and authorization across multiple supported access protocols). These protocols include POSIX, NFS, SMB, Hadoop, and Object (REST). For automated data management, it is equipped with powerful information lifecycle management (ILM) tools that can help administer unstructured data by providing the correct security for the correct data. This IBM RedpaperTM publication details the various aspects of security in IBM Spectrum ScaleTM, including the following items: Security of data in transit Security of data at rest Authentication Authorization Hadoop security Immutability Secure administration Audit logging Security for transparent cloud tiering (TCT) Security for OpenStack drivers Unless stated otherwise, the functions that are mentioned in this paper are available in IBM Spectrum Scale V4.2.1 or later releases.

IBM Software-Defined Storage Guide

Author: Larry Coyne

Publisher: IBM Redbooks

Published: 2018-07-21

Total Pages: 158

ISBN-13: 0738457051

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Today, new business models in the marketplace coexist with traditional ones and their well-established IT architectures. They generate new business needs and new IT requirements that can only be satisfied by new service models and new technological approaches. These changes are reshaping traditional IT concepts. Cloud in its three main variants (Public, Hybrid, and Private) represents the major and most viable answer to those IT requirements, and software-defined infrastructure (SDI) is its major technological enabler. IBM® technology, with its rich and complete set of storage hardware and software products, supports SDI both in an open standard framework and in other vendors' environments. IBM services are able to deliver solutions to the customers with their extensive knowledge of the topic and the experiences gained in partnership with clients. This IBM RedpaperTM publication focuses on software-defined storage (SDS) and IBM Storage Systems product offerings for software-defined environments (SDEs). It also provides use case examples across various industries that cover different client needs, proposed solutions, and results. This paper can help you to understand current organizational capabilities and challenges, and to identify specific business objectives to be achieved by implementing an SDS solution in your enterprise.

Book Synopsis IBM Software-Defined Storage Guide by : Larry Coyne

Download or read book IBM Software-Defined Storage Guide written by Larry Coyne and published by IBM Redbooks. This book was released on 2018-07-21 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, new business models in the marketplace coexist with traditional ones and their well-established IT architectures. They generate new business needs and new IT requirements that can only be satisfied by new service models and new technological approaches. These changes are reshaping traditional IT concepts. Cloud in its three main variants (Public, Hybrid, and Private) represents the major and most viable answer to those IT requirements, and software-defined infrastructure (SDI) is its major technological enabler. IBM® technology, with its rich and complete set of storage hardware and software products, supports SDI both in an open standard framework and in other vendors' environments. IBM services are able to deliver solutions to the customers with their extensive knowledge of the topic and the experiences gained in partnership with clients. This IBM RedpaperTM publication focuses on software-defined storage (SDS) and IBM Storage Systems product offerings for software-defined environments (SDEs). It also provides use case examples across various industries that cover different client needs, proposed solutions, and results. This paper can help you to understand current organizational capabilities and challenges, and to identify specific business objectives to be achieved by implementing an SDS solution in your enterprise.

Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers

Author: Scott Vetter

Publisher: IBM Redbooks

Published: 2018-01-31

Total Pages: 82

ISBN-13: 0738456608

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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.

Book Synopsis Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers by : Scott Vetter

Download or read book Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers written by Scott Vetter and published by IBM Redbooks. This book was released on 2018-01-31 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

SAP HANA on IBM Power Systems: High Availability and Disaster Recovery Implementation Updates

Author: Dino Quintero

Publisher: IBM Redbooks

Published: 2019-07-16

Total Pages: 186

ISBN-13: 073845785X

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This IBM® Redbooks® publication updates Implementing High Availability and Disaster Recovery Solutions with SAP HANA on IBM Power Systems, REDP-5443 with the latest technical content that describes how to implement an SAP HANA on IBM Power SystemsTM high availability (HA) and disaster recovery (DR) solution by using theoretical knowledge and sample scenarios. This book describes how all the pieces of the reference architecture work together (IBM Power Systems servers, IBM Storage servers, IBM SpectrumTM Scale, IBM PowerHA® SystemMirror® for Linux, IBM VM Recovery Manager DR for Power Systems, and Linux distributions) and demonstrates the resilience of SAP HANA with IBM Power Systems servers. This publication is for architects, brand specialists, distributors, resellers, and anyone developing and implementing SAP HANA on IBM Power Systems integration, automation, HA, and DR solutions. This publication provides documentation to transfer the how-to-skills to the technical teams, and documentation to the sales team.

Book Synopsis SAP HANA on IBM Power Systems: High Availability and Disaster Recovery Implementation Updates by : Dino Quintero

Download or read book SAP HANA on IBM Power Systems: High Availability and Disaster Recovery Implementation Updates written by Dino Quintero and published by IBM Redbooks. This book was released on 2019-07-16 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks® publication updates Implementing High Availability and Disaster Recovery Solutions with SAP HANA on IBM Power Systems, REDP-5443 with the latest technical content that describes how to implement an SAP HANA on IBM Power SystemsTM high availability (HA) and disaster recovery (DR) solution by using theoretical knowledge and sample scenarios. This book describes how all the pieces of the reference architecture work together (IBM Power Systems servers, IBM Storage servers, IBM SpectrumTM Scale, IBM PowerHA® SystemMirror® for Linux, IBM VM Recovery Manager DR for Power Systems, and Linux distributions) and demonstrates the resilience of SAP HANA with IBM Power Systems servers. This publication is for architects, brand specialists, distributors, resellers, and anyone developing and implementing SAP HANA on IBM Power Systems integration, automation, HA, and DR solutions. This publication provides documentation to transfer the how-to-skills to the technical teams, and documentation to the sales team.

AI and Big Data on IBM Power Systems Servers

Author: Scott Vetter

Publisher: IBM Redbooks

Published: 2019-04-10

Total Pages: 162

ISBN-13: 0738457515

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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.

Book Synopsis AI and Big Data on IBM Power Systems Servers by : Scott Vetter

Download or read book AI and Big Data on IBM Power Systems Servers written by Scott Vetter and published by IBM Redbooks. This book was released on 2019-04-10 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Platform Computing Solutions for High Performance and Technical Computing Workloads

Author: Dino Quintero

Publisher: IBM Redbooks

Published: 2015-06-19

Total Pages: 176

ISBN-13: 0738440752

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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.

Book Synopsis IBM Platform Computing Solutions for High Performance and Technical Computing Workloads by : Dino Quintero

Download or read book IBM Platform Computing Solutions for High Performance and Technical Computing Workloads written by Dino Quintero and published by IBM Redbooks. This book was released on 2015-06-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.