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.

Download or read book IBM Spectrum Scale written by Wei G. Gong and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Enabling Hybrid Cloud Storage for IBM Spectrum Scale Using Transparent Cloud Tiering written by Nikhil Khandelwal and published by IBM Redbooks. This book was released on 2018-05-31 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks® publication provides information to help you with the sizing, configuration, and monitoring of hybrid cloud solutions using the transparent cloud tiering (TCT) functionality of IBM SpectrumTM Scale. IBM Spectrum ScaleTM is a scalable data, file, and object management solution that provides a global namespace for large data sets and several enterprise features. The IBM Spectrum Scale feature called transparent cloud tiering allows cloud object storage providers, such as IBM CloudTM Object Storage, IBM Cloud, and Amazon S3, to be used as a storage tier for IBM Spectrum Scale. Transparent cloud tiering can help cut storage capital and operating costs by moving data that does not require local performance to an on-premise or off-premise cloud object storage provider. Transparent cloud tiering reduces the complexity of cloud object storage by making data transfers transparent to the user or application. This capability can help you adapt to a hybrid cloud deployment model where active data remains directly accessible to your applications and inactive data is placed in the correct cloud (private or public) automatically through IBM Spectrum Scale policies. This publication is intended for IT architects, IT administrators, storage administrators, and those wanting to learn more about sizing, configuration, and monitoring of hybrid cloud solutions using IBM Spectrum Scale and transparent cloud tiering.

Download or read book IBM Software Defined Infrastructure for Big Data Analytics Workloads written by Dino Quintero and published by IBM Redbooks. This book was released on 2015-06-29 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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.

Download or read book IBM Data Engine for Hadoop and Spark written by Dino Quintero and published by IBM Redbooks. This book was released on 2016-08-24 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Integration of IBM Aspera Sync with IBM Spectrum Scale Protecting and Sharing Files Globally written by Nils Haustein and published by IBM Redbooks. This book was released on 2019-03-29 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic globalization requires data to be available globally. With most data stored in file systems, solutions to make this data globally available become more important. Files that are in file systems can be protected or shared by replicating these files to another file system that is in a remote location. The remote location might be just around the corner or in a different country. Therefore, the techniques that are used to protect and share files must account for long distances and slow and unreliable wide area network (WAN) connections. IBM® Spectrum Scale is a scalable clustered file system that can be used to store all kinds of unstructured data. It provides open data access by way of Network File System (NFS); Server Message Block (SMB); POSIX Object Storage APIs, such as S3 and OpenStack Swift; and the Hadoop Distributed File System (HDFS) for accessing and sharing data. The IBM Aspera® file transfer solution (IBM Aspera Sync) provides predictable and reliable data transfer across large distance for small and large files. The combination of both can be used for global sharing and protection of data. This IBM RedpaperTM publication describes how IBM Aspera Sync can be used to protect and share data that is stored in IBM SpectrumTM Scale file systems across large distances of several hundred to thousands of miles. We also explain the integration of IBM Aspera Sync with IBM Spectrum ScaleTM and differentiate it from solutions that are built into IBM Spectrum Scale for protection and sharing. We also describe different use cases for IBM Aspera Sync with IBM Spectrum Scale.

Download or read book Cloud Data Sharing with IBM Spectrum Scale written by Nikhil Khandelwal and published by IBM Redbooks. This book was released on 2017-02-14 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® RedpaperTM publication provides information to help you with the sizing, configuration, and monitoring of hybrid cloud solutions using the Cloud data sharing feature of IBM Spectrum ScaleTM. IBM Spectrum Scale, formerly IBM General Parallel File System (IBM GPFSTM), is a scalable data and file management solution that provides a global namespace for large data sets along with several enterprise features. Cloud data sharing allows for the sharing and use of data between various cloud object storage types and IBM Spectrum Scale. Cloud data sharing can help with the movement of data in both directions, between file systems and cloud object storage, so that data is where it needs to be, when it needs to be there. This paper is intended for IT architects, IT administrators, storage administrators, and those who want to learn more about sizing, configuration, and monitoring of hybrid cloud solutions using IBM Spectrum Scale and Cloud data sharing.

Download or read book Making Data Smarter with IBM Spectrum Discover Practical AI Solutions written by Ivaylo B. Bozhinov and published by IBM Redbooks. This book was released on 2020-10-19 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data, such as the following examples: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM® Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on-premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum® Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research. This IBM Redbooks® publication presents several use cases that are focused on artificial intelligence (AI) solutions with IBM Spectrum Discover. This book helps storage administrators and technical specialists plan and implement AI solutions by using IBM Spectrum Discover and several other IBM Storage products.

Download or read book Implementation Guide for IBM Elastic Storage System 5000 written by Brian Herr and published by IBM Redbooks. This book was released on 2020-12-08 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks® publication introduces and describes the IBM Elastic Storage® Server 5000 (ESS 5000) as a scalable, high-performance data and file management solution. The solution is built on proven IBM Spectrum® Scale technology, formerly IBM General Parallel File System (IBM GPFS). ESS is a modern implementation of software-defined storage, making it easier for you to deploy fast, highly scalable storage for AI and big data. With the lightning-fast NVMe storage technology and industry-leading file management capabilities of IBM Spectrum Scale, the ESS 3000 and ESS 5000 nodes can grow to over YB scalability and can be integrated into a federated global storage system. By consolidating storage requirements from the edge to the core data center — including kubernetes and Red Hat OpenShift — IBM ESS can reduce inefficiency, lower acquisition costs, simplify storage management, eliminate data silos, support multiple demanding workloads, and deliver high performance throughout your organization. This book provides a technical overview of the ESS 5000 solution and helps you to plan the installation of the environment. We also explain the use cases where we believe it fits best. Our goal is to position this book as the starting point document for customers that would use the ESS 5000 as part of their IBM Spectrum Scale setups. This book is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for delivering cost-effective storage solutions with ESS 5000.

Download or read book IBM Spectrum Discover Metadata Management for Deep Insight of Unstructured Storage written by Joseph Dain and published by IBM Redbooks. This book was released on 2019-10-01 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication provides a comprehensive overview of the IBM Spectrum® Discover metadata management software platform. We give a detailed explanation of how the product creates, collects, and analyzes metadata. Several in-depth use cases are used that show examples of analytics, governance, and optimization. We also provide step-by-step information to install and set up the IBM Spectrum Discover trial environment. More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data such as: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.

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

Download or read book Data Accelerator for AI and Analytics written by Simon Lorenz and published by IBM Redbooks. This book was released on 2021-01-20 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.

Download or read book IBM Spectrum Scale Best Practices for Genomics Medicine Workloads written by Joanna Wong and published by IBM Redbooks. This book was released on 2018-04-25 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancing the science of medicine by targeting a disease more precisely with treatment specific to each patient relies on access to that patient's genomics information and the ability to process massive amounts of genomics data quickly. Although genomics data is becoming a critical source for precision medicine, it is expected to create an expanding data ecosystem. Therefore, hospitals, genome centers, medical research centers, and other clinical institutes need to explore new methods of storing, accessing, securing, managing, sharing, and analyzing significant amounts of data. Healthcare and life sciences organizations that are running data-intensive genomics workloads on an IT infrastructure that lacks scalability, flexibility, performance, management, and cognitive capabilities also need to modernize and transform their infrastructure to support current and future requirements. IBM® offers an integrated solution for genomics that is based on composable infrastructure. This solution enables administrators to build an IT environment in a way that disaggregates the underlying compute, storage, and network resources. Such a composable building block based solution for genomics addresses the most complex data management aspect and allows organizations to store, access, manage, and share huge volumes of genome sequencing data. IBM SpectrumTM Scale is software-defined storage that is used to manage storage and provide massive scale, a global namespace, and high-performance data access with many enterprise features. IBM Spectrum ScaleTM is used in clustered environments, provides unified access to data via file protocols (POSIX, NFS, and SMB) and object protocols (Swift and S3), and supports analytic workloads via HDFS connectors. Deploying IBM Spectrum Scale and IBM Elastic StorageTM Server (IBM ESS) as a composable storage building block in a Genomics Next Generation Sequencing deployment offers key benefits of performance, scalability, analytics, and collaboration via multiple protocols. This IBM RedpaperTM publication describes a composable solution with detailed architecture definitions for storage, compute, and networking services for genomics next generation sequencing that enable solution architects to benefit from tried-and-tested deployments, to quickly plan and design an end-to-end infrastructure deployment. The preferred practices and fully tested recommendations described in this paper are derived from running GATK Best Practices work flow from the Broad Institute. The scenarios provide all that is required, including ready-to-use configuration and tuning templates for the different building blocks (compute, network, and storage), that can enable simpler deployment and that can enlarge the level of assurance over the performance for genomics workloads. The solution is designed to be elastic in nature, and the disaggregation of the building blocks allows IT administrators to easily and optimally configure the solution with maximum flexibility. The intended audience for this paper is technical decision makers, IT architects, deployment engineers, and administrators who are working in the healthcare domain and who are working on genomics-based workloads.

Download or read book IBM Software Defined Infrastructure for Big Data Analytics Workloads written by Dino Quintero and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book documents how IBM Platform Computing, with its IBM Platform Symphony MapReduce framework, IBM Spectrum Scale (based upon IBM GPFS), IBM Platform LSF, the Advanced Service Controller for Platform Symphony work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offerings 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. --

Download or read book IBM Storage Solutions for SAS Analytics using IBM Spectrum Scale and IBM Elastic Storage System 3000 Version 1 Release 1 written by Sanjay Sudam and published by IBM Redbooks. This book was released on 2020-10-06 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper® publication is a blueprint for configuration, testing results, and tuning guidelines for running SAS workloads on Red Hat Enterprise Linux that use IBM Spectrum® Scale and IBM Elastic Storage® System (ESS) 3000. IBM lab validation was conducted with the Red Hat Linux nodes running with the SAS simulator scripts that are connected to the IBM Spectrum Scale and IBM ESS 3000. Simultaneous workloads are simulated across multiple x-86 nodes running with Red Hat Linux to determine scalability against the IBM Spectrum Scale clustered file system and ESS 3000 array. This paper outlines the architecture, configuration details, and performance tuning to maximize SAS application performance with the IBM Spectrum Scale 5.0.4.3 and IBM ESS 3000. This document is intended to facilitate the deployment and configuration of the SAS applications that use IBM Spectrum Scale and IBM Elastic Storage System (ESS) 3000. The information in this document is distributed on an "as is" basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Spectrum Scale or IBM ESS 3000 are supported and entitled and where the issues are specific to a blueprint implementation.

Download or read book IBM Elastic Storage Server Implementation Guide for Version 5 3 written by Luis Bolinches and published by IBM Redbooks. This book was released on 2019-02-05 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® RedpaperTM publication introduces and describes the IBM Elastic StorageTM Server as a scalable, high-performance data and file management solution. The solution is built on proven IBM SpectrumTM Scale technology, formerly IBM General Parallel File System (GPFSTM). IBM Elastic Storage Servers can be implemented for a range of diverse requirements, providing reliability, performance, and scalability. This publication helps you to understand the solution and its architecture and helps you to plan the installation and integration of the environment. The following combination of physical and logical components are required: Hardware Operating system Storage Network Applications This paper provides guidelines for several usage and integration scenarios. Typical scenarios include Cluster Export Services (CES) integration, disaster recovery, and multicluster integration. This paper addresses the needs of technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who must deliver cost-effective cloud services and big data solutions.