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Hortonworks Data Platform With Ibm Spectrum Scale Reference Guide For Building An Integrated Solution
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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.
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.
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.
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.
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.
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
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.
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.
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.
Book Synopsis Building Big Data and Analytics Solutions in the Cloud by : Wei-Dong Zhu
Download or read book Building Big Data and Analytics Solutions in the Cloud written by Wei-Dong Zhu and published by IBM Redbooks. This book was released on 2014-12-08 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis IBM Open Platform for DBaaS on IBM Power Systems by : Dino Quintero
Download or read book IBM Open Platform for DBaaS on IBM Power Systems written by Dino Quintero and published by IBM Redbooks. This book was released on 2018-03-26 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM Redbooks publication describes how to implement an Open Platform for Database as a Service (DBaaS) on IBM Power Systems environment for Linux, and demonstrate the open source tools, optimization and best practices guidelines for it. Open Platform for DBaaS on Power Systems is an on-demand, secure, and scalable self-service database platform that automates provisioning and administration of databases to support new business applications and information insights. This publication addresses topics to help sellers, architects, brand specialists, distributors, resellers and anyone offering secure and scalable Open Platform for DBaaS on Power Systems solution with APIs that are consistent across heterogeneous open database types. An Open Platform for DBaaS on Power Systems solution has the capability to accelerate business success by providing an infrastructure, and tools leveraging Open Source and OpenStack software engineered to optimize hardware and software between workloads and resources so you have a responsive, and an adaptive environment. Moreover, this publication provides documentation to transfer the how-to-skills for cloud oriented operational management of Open Platform for DBaaS on Power Systems service and underlying infrastructure to the technical teams. Open Platform for DBaaS on Power Systems mission is to provide scalable and reliable cloud database as a service provisioning functionality for both relational and non-relational database engines, and to continue to improve its fully-featured and extensible open source framework. For example, Trove is a database as a service for OpenStack. It is designed to run entirely on OpenStack, with the goal of allowing users to quickly and easily utilize the features of a relational or non-relational database without the burden of handling complex administrative tasks. Cloud users and database administrators can provision and manage multiple database instances as needed. Initially, the service focuses on providing resource isolation at high performance while automating complex administrative tasks including deployment, configuration, patching, backups, restores, and monitoring. In the context of this publication, the monitoring tool implemented is Nagios Core which is an open source monitoring tool. Hence, when you see a reference of Nagios in this book, Nagios Core is the open source monitoring solution implemented. Also note that the implementation of Open Platform for DBaaS on IBM Power Systems is based on open source solutions. This book is targeted toward sellers, architects, brand specialists, distributors, resellers and anyone developing and implementing Open Platform for DBaaS on Power Systems solutions.
Book Synopsis Implementation Guide for IBM Elastic Storage System 5000 by : Brian Herr
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.
Book Synopsis Implementation Guide for IBM Elastic Storage System 3000 by : Brian Herr
Download or read book Implementation Guide for IBM Elastic Storage System 3000 written by Brian Herr and published by IBM Redbooks. This book was released on 2021-06-28 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks publication introduces and describes the IBM Elastic Storage® Server 3000 (ESS 3000) 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). IBM Elastic Storage System 3000 is an all-Flash array platform. This storage platform uses NVMe-attached drives in ESS 3000 to provide significant performance improvements as compared to SAS-attached flash drives. This book provides a technical overview of the ESS 3000 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 ESS 3000 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 3000.
Book Synopsis IBM Software Defined Infrastructure for Big Data Analytics Workloads by : Dino Quintero
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 Briggs written by Barry Briggs and published by Microsoft Press. This book was released on 2016-01-07 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: How do you start? How should you build a plan for cloud migration for your entire portfolio? How will your organization be affected by these changes? This book, based on real-world cloud experiences by enterprise IT teams, seeks to provide the answers to these questions. Here, you’ll see what makes the cloud so compelling to enterprises; with which applications you should start your cloud journey; how your organization will change, and how skill sets will evolve; how to measure progress; how to think about security, compliance, and business buy-in; and how to exploit the ever-growing feature set that the cloud offers to gain strategic and competitive advantage.
Book Synopsis Cloud Data Sharing with IBM Spectrum Scale by : Nikhil Khandelwal
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.
Book Synopsis The Journey Continues: From Data Lake to Data-Driven Organization by : Mandy Chessell
Download or read book The Journey Continues: From Data Lake to Data-Driven Organization written by Mandy Chessell and published by IBM Redbooks. This book was released on 2018-02-19 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM RedguideTM publication looks back on the key decisions that made the data lake successful and looks forward to the future. It proposes that the metadata management and governance approaches developed for the data lake can be adopted more broadly to increase the value that an organization gets from its data. Delivering this broader vision, however, requires a new generation of data catalogs and governance tools built on open standards that are adopted by a multi-vendor ecosystem of data platforms and tools. Work is already underway to define and deliver this capability, and there are multiple ways to engage. This guide covers the reasons why this new capability is critical for modern businesses and how you can get value from it.
