Moyens d'évaluation :
- Quiz pré-formation de vérification des connaissances (si applicable)
- Évaluations formatives pendant la formation, à travers les travaux pratiques réalisés sur les labs à l’issue de chaque module, QCM, mises en situation…
- Complétion par chaque participant d’un questionnaire et/ou questionnaire de positionnement en amont et à l’issue de la formation pour validation de l’acquisition des compétences
Who should attend
This lecture and exercise-based course is for individuals who want to configure a Spectrum Scale cluster to allow remote data access.
Prerequisites
Students must already know the basics of installing, configuring and managing a Spectrum Scale clustered file system. This prerequisite can be met by attending the following courses:
H005G - IBM Spectrum Scale Basic Administration for Linux and AIX
H006G - IBM Spectrum Scale Advanced Administration for Linux
Course Content
Enterprises and organizations are creating, analyzing and keeping more data than ever before. An organization’s underlying storage must support new-era big data and artificial intelligence workloads along with traditional applications while ensuring security, reliability and high performance. IBM Spectrum Scale meets these challenges as a high-performance solution for managing data at scale. This new course covers IBM Spectrum Scale features that enable data-anywhere access that spans storage and locations to accelerate applications across the data center or around the world. Attendees should already know the basics of installing, configuring and managing a Spectrum Scale clustered file system and how to use the installer toolkit.
This course is intended for IT professionals tasked with administering a Spectrum Scale storage cluster consisting of Linux nodes. The course includes information on various Spectrum Scale features that enable remote access to the data that is stored in a cluster file system. This includes: multi-cluster support, clustered NFS, cluster export services (CES) and protocol support (NFS, SMB, Object, and block), Active File Management (AFM), and AFM-based Asynchronous Disaster Recovery (AFM DR). The features are described in lecture materials and implemented in lab exercises.
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