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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.
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.
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.
Economic globalization requires data to be available globally. With most data stored in file systems, solutions to make this data globally available become more important. Files that are in file systems can be protected or shared by replicating these files to another file system that is in a remote location. The remote location might be just around the corner or in a different country. Therefore, the techniques that are used to protect and share files must account for long distances and slow and unreliable wide area network (WAN) connections. IBM® Spectrum Scale is a scalable clustered file system that can be used to store all kinds of unstructured data. It provides open data access by way of Network File System (NFS); Server Message Block (SMB); POSIX Object Storage APIs, such as S3 and OpenStack Swift; and the Hadoop Distributed File System (HDFS) for accessing and sharing data. The IBM Aspera® file transfer solution (IBM Aspera Sync) provides predictable and reliable data transfer across large distance for small and large files. The combination of both can be used for global sharing and protection of data. This IBM RedpaperTM publication describes how IBM Aspera Sync can be used to protect and share data that is stored in IBM SpectrumTM Scale file systems across large distances of several hundred to thousands of miles. We also explain the integration of IBM Aspera Sync with IBM Spectrum ScaleTM and differentiate it from solutions that are built into IBM Spectrum Scale for protection and sharing. We also describe different use cases for IBM Aspera Sync with IBM Spectrum Scale.
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.
This IBM® Redbooks® publication provides topics to help the technical community take advantage of the resilience, scalability, and performance of the IBM Power SystemsTM platform to implement or integrate an IBM Data Engine for Hadoop and Spark solution for analytics solutions to access, manage, and analyze data sets to improve business outcomes. This book documents topics to demonstrate and take advantage of the analytics strengths of the IBM POWER8® platform, the IBM analytics software portfolio, and selected third-party tools to help solve customer's data analytic workload requirements. This book describes how to plan, prepare, install, integrate, manage, and show how to use the IBM Data Engine for Hadoop and Spark solution to run analytic workloads on IBM POWER8. In addition, this publication delivers documentation to complement available IBM analytics solutions to help your data analytic needs. This publication strengthens the position of IBM analytics and big data solutions with a well-defined and documented deployment model within an IBM POWER8 virtualized environment so that customers have a planned foundation for security, scaling, capacity, resilience, and optimization for analytics workloads. This book is targeted at technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) that are responsible for delivering analytics solutions and support on IBM Power Systems.
This IBM® Redpaper publication provides a comprehensive overview of the IBM Spectrum® Discover metadata management software platform. We give a detailed explanation of how the product creates, collects, and analyzes metadata. Several in-depth use cases are used that show examples of analytics, governance, and optimization. We also provide step-by-step information to install and set up the IBM Spectrum Discover trial environment. More than 80% of all data that is collected by organizations is not in a standard relational database. Instead, it is trapped in unstructured documents, social media posts, machine logs, and so on. Many organizations face significant challenges to manage this deluge of unstructured data such as: Pinpointing and activating relevant data for large-scale analytics Lacking the fine-grained visibility that is needed to map data to business priorities Removing redundant, obsolete, and trivial (ROT) data Identifying and classifying sensitive data IBM Spectrum Discover is a modern metadata management software that provides data insight for petabyte-scale file and Object Storage, storage on premises, and in the cloud. This software enables organizations to make better business decisions and gain and maintain a competitive advantage. IBM Spectrum Discover provides a rich metadata layer that enables storage administrators, data stewards, and data scientists to efficiently manage, classify, and gain insights from massive amounts of unstructured data. It improves storage economics, helps mitigate risk, and accelerates large-scale analytics to create competitive advantage and speed critical research.
Having appropriate storage for hosting business-critical data and advanced Security Information and Event Management (SIEM) software for deep inspection, detection, and prioritization of threats has become a necessity for any business. This IBM® Redpaper publication explains how the storage features of IBM Spectrum® Scale, when combined with the log analysis, deep inspection, and detection of threats that are provided by IBM QRadar®, help reduce the impact of incidents on business data. Such integration provides an excellent platform for hosting unstructured business data that is subject to regulatory compliance requirements. This paper describes how IBM Spectrum Scale File Audit Logging can be integrated with IBM QRadar. Using IBM QRadar, an administrator can monitor, inspect, detect, and derive insights for identifying potential threats to the data that is stored on IBM Spectrum Scale. When the threats are identified, you can quickly act on them to mitigate or reduce the impact of incidents. We further demonstrate how the threat detection by IBM QRadar can proactively trigger data snapshots or cyber resiliency workflow in IBM Spectrum Scale to protect the data during threat. This third edition has added the section "Ransomware threat detection", where we describe a ransomware attack scenario within an environment to leverage IBM Spectrum Scale File Audit logs integration with IBM QRadar. This paper is intended for chief technology officers, solution engineers, security architects, and systems administrators. This paper assumes a basic understanding of IBM Spectrum Scale and IBM QRadar and their administration.
This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.