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High Performant File System Workloads For Ai And Hpc On Aws Using Ibm Spectrum Scale
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Book Synopsis High Performant File System Workloads for AI and HPC on AWS using IBM Spectrum Scale by : Sanjay Sudam
Download or read book High Performant File System Workloads for AI and HPC on AWS using IBM Spectrum Scale written by Sanjay Sudam and published by IBM Redbooks. This book was released on 2021-03-31 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper® publication is intended to facilitate the deployment and configuration of the IBM Spectrum® Scale based high-performance storage solutions for the scalable data and AI solutions on Amazon Web Services (AWS). Configuration, testing results, and tuning guidelines for running the IBM Spectrum Scale based high-performance storage solutions for the data and AI workloads on AWS are the focus areas of the paper. The LAB Validation was conducted with the Red Hat Linux nodes to IBM Spectrum Scale by using the various Amazon Elastic Compute Cloud (EC2) instances. Simultaneous workloads are simulated across multiple Amazon EC2 nodes running with Red Hat Linux to determine scalability against the IBM Spectrum Scale clustered file system. Solution architecture, configuration details, and performance tuning demonstrate how to maximize data and AI application performance with IBM Spectrum Scale on AWS.
Book Synopsis IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences by : Dino Quintero
Download or read book IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences written by Dino Quintero and published by IBM Redbooks. This book was released on 2019-09-08 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication provides an update to the original description of IBM Reference Architecture for Genomics. This paper expands the reference architecture to cover all of the major vertical areas of healthcare and life sciences industries, such as genomics, imaging, and clinical and translational research. The architecture was renamed IBM Reference Architecture for High Performance Data and AI in Healthcare and Life Sciences to reflect the fact that it incorporates key building blocks for high-performance computing (HPC) and software-defined storage, and that it supports an expanding infrastructure of leading industry partners, platforms, and frameworks. The reference architecture defines a highly flexible, scalable, and cost-effective platform for accessing, managing, storing, sharing, integrating, and analyzing big data, which can be deployed on-premises, in the cloud, or as a hybrid of the two. IT organizations can use the reference architecture as a high-level guide for overcoming data management challenges and processing bottlenecks that are frequently encountered in personalized healthcare initiatives, and in compute-intensive and data-intensive biomedical workloads. This reference architecture also provides a framework and context for modern healthcare and life sciences institutions to adopt cutting-edge technologies, such as cognitive life sciences solutions, machine learning and deep learning, Spark for analytics, and cloud computing. To illustrate these points, this paper includes case studies describing how clients and IBM Business Partners alike used the reference architecture in the deployments of demanding infrastructures for precision medicine. This publication targets technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing life sciences solutions and support.
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 Monitoring Overview for IBM Spectrum Scale and IBM Elastic Storage Server by : Kedar Karmarkar
Download or read book Monitoring Overview for IBM Spectrum Scale and IBM Elastic Storage Server written by Kedar Karmarkar and published by IBM Redbooks. This book was released on 2017-07-28 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: IBM® Spectrum Scale is software-defined storage for high-performance, large-scale workloads. IBM SpectrumTM Scale (formerly IBM General parallel file system or GPFS) is a scalable data and file management solution that provides a global namespace for large data sets along with several enterprise features. IBM Spectrum ScaleTM is used in clustered environments and provides file protocol (POSIX, NFS, and SMB) and object protocol (Swift and S3) access methods. IBM Elastic StorageTM Server (ESS) is a software-defined storage system that is built upon proven IBM Power SystemsTM, IBM Spectrum Scale software, and storage enclosures. ESS allows for capacity scale up or scale out for performance in modular building blocks, which enables sharing for large data sets across workloads with unified storage pool for file, object, and Hadoop workloads. ESS uses erasure coding-based declustered RAID technology that was developed by IBM to rebuild failed disks in few minutes instead of days. IBM ESS and IBM Spectrum Scale are implemented in scalable environments that are running enterprise workloads. ESS and IBM Spectrum Scale are key components of the enterprise infrastructure. With growing expectations of availability on enterprise infrastructures, monitoring IBM Spectrum Scale, ESS health, and performance is an important function for any IT administrator. This IBM RedpaperTM publication provides an overview of key parameters and methods of IBM Spectrum Scale and ESS monitoring. The audience for this document is IT architects, IT administrators, storage administrators, and users who want to learn more about the administration of an IBM Spectrum Scale and ESS system. This document can be used to monitorfor the environments with IBM Spectrum Scale version 4.2.2.X0 or later. The examples in the document are based on IBM Spectrum Scale 4.2.2.X and ESS 5.0.X.X versions.
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 2019-07-17 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 A Deployment Guide for IBM Spectrum Scale Unified File and Object Storage by : Dean Hildebrand
Download or read book A Deployment Guide for IBM Spectrum Scale Unified File and Object Storage written by Dean Hildebrand and published by IBM Redbooks. This book was released on 2017-05-24 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because of the explosion of unstructured data that is generated by individuals and organizations, a new storage paradigm that is called object storage has been developed. Object storage stores data in a flat namespace that scales to trillions of objects. The design of object storage also simplifies how users access data, supporting new types of applications and allowing users to access data by using various methods, including mobile devices and web applications. Data distribution and management are also simplified, allowing greater collaboration across the globe. OpenStack Swift is an emerging open source object storage software platform that is widely used for cloud storage. IBM® Spectrum Scale, which is based on IBM General Parallel File System (IBM GPFSTM) technology, is a high-performance and proven product that is used to store data for thousands of mission-critical commercial installations worldwide. Throughout this IBM RedpaperTM publication, IBM SpectrumTM Scale is used to refer to GPFS. The examples in this paper are based on IBM Spectrum ScaleTM V4.2.2. IBM Spectrum Scale also automates common storage management tasks, such as tiering and archiving at scale. Together, IBM Spectrum Scale and OpenStack Swift provide an enterprise-class object storage solution that efficiently stores, distributes, and retains critical data. This paper provides instructions about setting up and configuring IBM Spectrum Scale Object Storage that is based on OpenStack Swift. It also provides an initial set of preferred practices that ensure optimal performance and reliability. This paper is intended for administrators who are familiar with IBM Spectrum Scale and OpenStack Swift components.
Book Synopsis IBM Spectrum Scale CSI Driver for Container Persistent Storage by : Abhishek Jain
Download or read book IBM Spectrum Scale CSI Driver for Container Persistent Storage written by Abhishek Jain and published by IBM Redbooks. This book was released on 2020-04-10 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: IBM® Spectrum Scale is a proven, scalable, high-performance data and file management solution. It provides world-class storage management with extreme scalability, flash accelerated performance, automatic policy-based storage that has tiers of flash through disk to tape. It also provides support for various protocols, such as NFS, SMB, Object, HDFS, and iSCSI. Containers can leverage the performance, information lifecycle management (ILM), scalability, and multisite data management to give the full flexibility on storage as they experience on the runtime. Container adoption is increasing in all industries, and they sprawl across multiple nodes on a cluster. The effective management of containers is necessary because their number will probably reach a far greater number than virtual machines today. Kubernetes is the standard container management platform currently being used. Data management is of ultimate importance, and often is forgotten because the first workloads containerized are ephemeral. For data management, many drivers with different specifications were available. A specification named Container Storage Interface (CSI) was created and is now adopted by all major Container Orchestrator Systems available. Although other container orchestration systems exist, Kubernetes became the standard framework for container management. It is a very flexible open source platform used as the base for most cloud providers and software companies' container orchestration systems. Red Hat OpenShift is one of the most reliable enterprise-grade container orchestration systems based on Kubernetes, designed and optimized to easily deploy web applications and services. OpenShift enables developers to focus on the code, while the platform takes care of all of the complex IT operations and processes. This IBM Redbooks® publication describes how the CSI Driver for IBM file storage enables IBM Spectrum® Scale to be used as persistent storage for stateful applications running in Kubernetes clusters. Through the Container Storage Interface Driver for IBM file storage, Kubernetes persistent volumes (PVs) can be provisioned from IBM Spectrum Scale. Therefore, the containers can be used with stateful microservices, such as database applications (MongoDB, PostgreSQL, and so on).
Book Synopsis IBM Hybrid Solution for Scalable Data Solutions using IBM Spectrum Scale by : IBM
Download or read book IBM Hybrid Solution for Scalable Data Solutions using IBM Spectrum Scale written by IBM and published by IBM Redbooks. This book was released on 2019-07-02 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document is intended to facilitate the deployment of the scalable hybrid cloud solution for data agility and collaboration using IBM® Spectrum Scale across multiple public clouds. To complete the tasks it describes, you must understand IBM Spectrum Scale and IBM Spectrum Scale Active File Management (AFM). The information in this document is distributed on an 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 Spectrum Scale Active File Management are supported and entitled, and where the issues are specific to a blueprint implementation.
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 Enabling Hybrid Cloud Storage for IBM Spectrum Scale Using Transparent Cloud Tiering by : Nikhil Khandelwal
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 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:
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 IBM High-Performance Computing Insights with IBM Power System AC922 Clustered Solution by : Dino Quintero
Download or read book IBM High-Performance Computing Insights with IBM Power System AC922 Clustered Solution written by Dino Quintero and published by IBM Redbooks. This book was released on 2019-05-02 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redbooks® publication documents and addresses topics to set up a complete infrastructure environment and tune the applications to use an IBM POWER9TM hardware architecture with the technical computing software stack. This publication is driven by a CORAL project solution. It explores, tests, and documents how to implement an IBM High-Performance Computing (HPC) solution on a POWER9 processor-based system by using IBM technical innovations to help solve challenging scientific, technical, and business problems. This book documents the HPC clustering solution with InfiniBand on IBM Power SystemsTM AC922 8335-GTH and 8335-GTX servers with NVIDIA Tesla V100 SXM2 graphics processing units (GPUs) with NVLink, software components, and the IBM SpectrumTM Scale parallel file system. This solution includes recommendations about the components that are used to provide a cohesive clustering environment that includes job scheduling, parallel application tools, scalable file systems, administration tools, and a high-speed interconnect. This book is divided into three parts: Part 1 focuses on the planners of the solution, Part 2 focuses on the administrators, and Part 3 focuses on the developers. This book targets technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for delivering cost-effective HPC solutions that help uncover insights among clients' data so that they can act to optimize business results, product development, and scientific discoveries.
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.
Book Synopsis Applied Machine Learning and High-Performance Computing on AWS by : Mani Khanuja
Download or read book Applied Machine Learning and High-Performance Computing on AWS written by Mani Khanuja and published by Packt Publishing Ltd. This book was released on 2022-12-30 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
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 IBM Elastic Storage Server Implementation Guide for Version 5.3 by : Luis Bolinches
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.
