Singlestore Database On High Performance Ibm Spectrum Scale Filesystem With Red Hat Openshift And Ibm Cloud Pak For Data
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| Author | : Nilesh Suryawanshi |
| Publisher | : IBM Redbooks |
| Total Pages | : 34 |
| Release | : 2022-09-15 |
| Genre | : Computers |
| ISBN | : 0738460818 |
This IBM® blueprint describes the SingleStoreDB that is running on Red Hat OpenShift in a containerized environment. The SingleStoreDB deployment uses the IBM Spectrum® Scale container native access storage class to create persistent volumes (PVs) for the SingleStoreDB pods deployment. This document also describes the process that is used to expand a SingleStoreDB volume on IBM Spectrum Scale and an IBM Spectrum Scale PV on a Red Hat OpenShift cluster for IBM Spectrum Scale to verify that the SingleStoreDB remained intact after the volume is expanded. The procedure to create a sample database that is named stockDB, and the data analytical stats for reading and writing the data also are included. The sample data was captured for comparison statistics for SingleStoreDB that is deployed on the IBM Spectrum Scale Cluster File System and local storage. These comparison statistics emphasize the notable difference between the sample data sets. Finally, this document also explains the procedure that is used to create the same sample database with the unlimited storage feature in SingleStore by using IBM Cloud® Object Storage.
| Author | : Gero Schmidt |
| Publisher | : IBM Redbooks |
| Total Pages | : 120 |
| Release | : 2021-12-17 |
| Genre | : Computers |
| ISBN | : 0738460095 |
This IBM® Redpaper® publication describes configuration guidelines and best practices when IBM Spectrum® Scale Container Native Storage Access is used as a storage provider for IBM Cloud® Pak for Data on Red Hat OpenShift Container Platform. It also provides the steps to install IBM Db2® and several assemblies within IBM Cloud Pak® for Data, including Watson Knowledge Catalog, Watson Studio, IBM DataStage®, Db2 Warehouse, Watson Machine Learning, Watson OpenScale, Data Virtualization, Data Management Console, and Apache Spark. This IBM Redpaper publication was written for IT architects, IT specialists, developers, and others who are interested in installing IBM Cloud Pak for Data with IBM Spectrum Scale Container Native.
| Author | : Abhishek Jain |
| Publisher | : IBM Redbooks |
| Total Pages | : 90 |
| Release | : 2020-04-10 |
| Genre | : Computers |
| ISBN | : 0738458643 |
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).
| Author | : IBM |
| Publisher | : IBM Redbooks |
| Total Pages | : 28 |
| Release | : 2019-09-29 |
| Genre | : Computers |
| ISBN | : 0738458082 |
IBM Storage for Red Hat OpenShift Container Platform is a comprehensive container-ready solution that includes all the hardware & software components necessary to setup and/or expand your Red Hat OpenShift Container Platform V3.11 environment. IBM Storage, bringing enterprise data services to containers. In this blueprint, learn how to: • Combine the benefits of IBM Systems with the performance of IBM Storage solutions so that you can deliver the right services to your clients today! • Build a 24 by 7 by 365 enterprise class private cloud with Red Hat OpenShift Container Platform • Leverage enterprise class services such as NVMe based flash performance, high data availability, and advanced container security IBM Storage for Red Hat OpenShift Container Platform: designed for your DevOps environment for on-premises deployment with easy-to-consume components built to perform and scale for your enterprise. Simplify your journey to cloud with pre-tested and validated blueprints engineered to enable rapid deployment and peace of mind as you move to a hybrid multicloud environment. You now have the capabilities.
| Author | : Dean Hildebrand |
| Publisher | : IBM Redbooks |
| Total Pages | : 74 |
| Release | : 2017-05-24 |
| Genre | : Computers |
| ISBN | : 0738455997 |
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.
| Author | : Seth Earley |
| Publisher | : Lifetree Media |
| Total Pages | : 320 |
| Release | : 2020-04-28 |
| Genre | : Business & Economics |
| ISBN | : 9781928055501 |
Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business. The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future? When the Web came along in the mid-90s, it transformed the behavior of customers and remade whole industries. Now, as part of its promise to bring revolutionary change in untold ways to human activity, artificial intelligence--AI--is about to create another complete transformation in how companies create and deliver value to customers. But despite the billions spent so far on bots and other tools, AI continues to stumble. Why can't it magically use all the data organizations generate to make them run faster and better? Because something is missing. AI works only when it understands the soul of the business. An ontology is a holistic digital model of every piece of information that matters to the business, from processes to products to people, and it's what makes the difference between the promise of AI and delivering on that promise. Business leaders who want to catch the AI wave--rather than be crushed by it--need to read The AI-Powered Enterprise. The book is the first to combine a sophisticated explanation of how AI works with a practical approach to applying AI to the problems of business, from customer experience to business operations to product development.
| Author | : Nikhil Khandelwal |
| Publisher | : IBM Redbooks |
| Total Pages | : 36 |
| Release | : 2017-02-14 |
| Genre | : Computers |
| ISBN | : 0738456004 |
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.
| Author | : IBM |
| Publisher | : IBM Redbooks |
| Total Pages | : 54 |
| Release | : 2021-12-20 |
| Genre | : Computers |
| ISBN | : 0738460141 |
Cyberattacks are likely to remain a significant risk for the foreseeable future. Attacks on organizations can be external and internal. Investing in technology and processes to prevent these cyberattacks is the highest priority for these organizations. Organizations need well-designed procedures and processes to recover from attacks. The focus of this document is to demonstrate how the IBM® Unified Data Foundation (UDF) infrastructure plays an important role in delivering the persistence storage (PV) to containerized applications, such as IBM Cloud® Pak for Security (CP4S), with IBM Spectrum® Scale Container Native Storage Access (CNSA) that is deployed with IBM Spectrum scale CSI driver and IBM FlashSystem® storage with IBM Block storage driver with CSI driver. Also demonstrated is how this UDF infrastructure can be used as a preferred storage class to create back-end persistent storage for CP4S deployments. We also highlight how the file I/O events are captured in IBM QRadar® and offenses are generated based on predefined rules. After the offenses are generated, we show how the cases are automatically generated in IBM Cloud Pak® for Security by using the IBM QRadar SOAR Plugin, with a manually automated method to log a case in IBM Cloud Pak for Security. This document also describes the processes that are required for the configuration and integration of the components in this solution, such as: Integration of IBM Spectrum Scale with QRadar QRadar integration with IBM Cloud Pak for Security Integration of the IBM QRadar SOAR Plugin to generate automated cases in CP4S. Finally, this document shows the use of IBM Spectrum Scale CNSA and IBM FlashSystem storage that uses IBM block CSI driver to provision persistent volumes for CP4S deployment. All models of IBM FlashSystem family are supported by this document, including: FlashSystem 9100 and 9200 FlashSystem 7200 and FlashSystem 5000 models FlashSystem 5200 IBM SAN Volume Controller All storage that is running IBM Spectrum Virtualize software
| Author | : Hemanth Manda |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 337 |
| Release | : 2021-11-24 |
| Genre | : Computers |
| ISBN | : 1800567405 |
Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.
| Author | : Joseph Dain |
| Publisher | : IBM Redbooks |
| Total Pages | : 108 |
| Release | : 2020-08-11 |
| Genre | : Computers |
| ISBN | : 073845902X |
This IBM® Redpaper publication explains how IBM Spectrum® Discover integrates with the IBM Watson® Knowledge Catalog (WKC) component of IBM Cloud® Pak for Data (IBM CP4D) to make the enriched catalog content in IBM Spectrum Discover along with the associated data available in WKC and IBM CP4D. From an end-to-end IBM solution point of view, IBM CP4D and WKC provide state-of-the-art data governance, collaboration, and artificial intelligence (AI) and analytics tools, and IBM Spectrum Discover complements these features by adding support for unstructured data on large-scale file and object storage systems on premises and in the cloud. Many organizations face challenges to manage unstructured data. Some challenges that companies face include: Pinpointing and activating relevant data for large-scale analytics, machine learning (ML) and deep learning (DL) workloads. Lacking the fine-grained visibility that is needed to map data to business priorities. Removing redundant, obsolete, and trivial (ROT) data and identifying data that can be moved to a lower-cost storage tier. Identifying and classifying sensitive data as it relates to various compliance mandates, such as the General Data Privacy Regulation (GDPR), Payment Card Industry Data Security Standards (PCI-DSS), and the Health Information Portability and Accountability Act (HIPAA). This paper describes how IBM Spectrum Discover provides seamless integration of data in IBM Storage with IBM Watson Knowledge Catalog (WKC). Features include: Event-based cataloging and tagging of unstructured data across the enterprise. Automatically inspecting and classifying over 1000 unstructured data types, including genomics and imaging specific file formats. Automatically registering assets with WKC based on IBM Spectrum Discover search and filter criteria, and by using assets in IBM CP4D. Enforcing data governance policies in WKC in IBM CP4D based on insights from IBM Spectrum Discover, and using assets in IBM CP4D. Several in-depth use cases are used that show examples of healthcare, life sciences, and financial services. IBM Spectrum Discover integration with WKC enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of data. The integration improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
