Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale

Cloudera Data Platform Private Cloud Base with IBM Spectrum Scale
Author: Wei Gong
Publisher: IBM Redbooks
Total Pages: 42
Release: 2021-08-27
Genre: Computers
ISBN: 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

Enabling Hybrid Cloud Storage for IBM Spectrum Scale Using Transparent Cloud Tiering

Enabling Hybrid Cloud Storage for IBM Spectrum Scale Using Transparent Cloud Tiering
Author: Nikhil Khandelwal
Publisher: IBM Redbooks
Total Pages: 44
Release: 2018-05-31
Genre: Computers
ISBN: 0738456861
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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.

Implementing IBM Spectrum Virtualize for Public Cloud on AWS Version 8.3.1

Implementing IBM Spectrum Virtualize for Public Cloud on AWS Version 8.3.1
Author: Jordan Fincher
Publisher: IBM Redbooks
Total Pages: 142
Release: 2020-07-08
Genre: Computers
ISBN: 0738458961
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IBM® Spectrum Virtualize is a key member of the IBM Spectrum® Storage portfolio. It is a highly flexible storage solution that enables rapid deployment of block storage services for new and traditional workloads, whether on-premises, off-premises, or a combination of both. The initial release of IBM Spectrum Virtualize for Public Cloud is now available on Amazon Web Services (AWS). This IBM RedpaperTM publication gives a broad understanding of the IBM Spectrum Virtualize for Public Cloud on AWS architecture. It also provides planning and implementation information about the common use cases for this new product. This publication helps storage and networking administrators plan, implement, install, modify, and configure the IBM Spectrum Virtualize for Public Cloud on AWS offering Version 8.3.1. It also provides a detailed description of troubleshooting tips.

IBM Spectrum Scale Security

IBM Spectrum Scale Security
Author: Felipe Knop
Publisher: IBM Redbooks
Total Pages: 116
Release: 2018-09-18
Genre: Computers
ISBN: 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.

Building Big Data and Analytics Solutions in the Cloud

Building Big Data and Analytics Solutions in the Cloud
Author: Wei-Dong Zhu
Publisher: IBM Redbooks
Total Pages: 114
Release: 2014-12-08
Genre: Computers
ISBN: 0738453994
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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.

AI and Big Data on IBM Power Systems Servers

AI and Big Data on IBM Power Systems Servers
Author: Scott Vetter
Publisher: IBM Redbooks
Total Pages: 162
Release: 2019-04-10
Genre: Computers
ISBN: 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.

IBM Platform Computing Solutions

IBM Platform Computing Solutions
Author: Dino Quintero
Publisher: IBM Redbooks
Total Pages: 370
Release: 2012-12-07
Genre: Computers
ISBN: 0738437484
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This IBM® Platform Computing Solutions Redbooks® publication is the first book to describe each of the available offerings that are part of the IBM portfolio of Cloud, analytics, and High Performance Computing (HPC) solutions for our clients. This IBM Redbooks publication delivers descriptions of the available offerings from IBM Platform Computing that address challenges for our clients in each industry. We include a few implementation and testing scenarios with selected solutions. This publication helps strengthen the position of IBM Platform Computing solutions with a well-defined and documented deployment model within an IBM System x® environment. This deployment model offers clients a planned foundation for dynamic cloud infrastructure, provisioning, large-scale parallel HPC application development, cluster management, and grid applications. This IBM publication is targeted to IT specialists, IT architects, support personnel, and clients. This book is intended for anyone who wants information about how IBM Platform Computing solutions use IBM to provide a wide array of client solutions.

IBM Software Defined Infrastructure for Big Data Analytics Workloads

IBM Software Defined Infrastructure for Big Data Analytics Workloads
Author: Dino Quintero
Publisher: IBM Redbooks
Total Pages: 180
Release: 2015-06-29
Genre: Computers
ISBN: 0738440779
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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.

Enterprise Cloud Strategy

Enterprise Cloud Strategy
Author: Barry Briggs
Publisher: Microsoft Press
Total Pages: 228
Release: 2016-01-07
Genre: Computers
ISBN: 1509301992
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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.

Implementation Guide for IBM Elastic Storage System 5000

Implementation Guide for IBM Elastic Storage System 5000
Author: Brian Herr
Publisher: IBM Redbooks
Total Pages: 130
Release: 2020-12-08
Genre: Computers
ISBN: 0738459224
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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.