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Sumit Kumar

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Privileged Access Management for Secure Storage Administration: IBM Spectrum Scale with IBM Security Verify Privilege Vault

There is a growing insider security risk to organizations. Human error, privilege misuse, and cyberespionage are considered the top insider threats. One of the most dangerous internal security threats is the privileged user with access to critical data, which is the "crown jewels" of the organization. This data is on storage, so storage administration has critical privilege access that can cause major security breaches and jeopardize the safety of sensitive assets. Organizations must maintain tight control over whom they grant privileged identity status to for storage administration. Extra storage administration access must be shared with support and services teams when required. There also is a need to audit critical resource access that is required by compliance to standards and regulations. IBM® Security™ Verify Privilege Vault On-Premises (Verify Privilege Vault), formerly known as IBM Security™ Secret Server, is the next-generation privileged account management that integrates with IBM Storage to ensure that access to IBM Storage administration sessions is secure and monitored in real time with required recording for audit and compliance. Privilege access to storage administration sessions is centrally managed, and each session can be timebound with remote monitoring. You also can use remote termination and an approval workflow for the session. In this IBM Redpaper, we demonstrate the integration of IBM Spectrum® Scale and IBM Elastic Storage® Server (IBM ESS) with Verify Privilege Vault, and show how to use privileged access management (PAM) for secure storage administration. This paper is targeted at storage and security administrators, storage and security architects, and chief information security officers.

Implementation Guide for IBM Elastic Storage System 5000

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.

Apache Spark 2.x for Java Developers

Delve into mastering big data processing with 'Apache Spark 2.x for Java Developers.' This book provides a practical guide to implementing Apache Spark using the Java APIs, offering a unique opportunity for Java developers to leverage Spark's powerful framework without transitioning to Scala. What this Book will help me do Learn how to process data from formats like XML, JSON, CSV using Spark Core. Implement real-time analytics using Spark Streaming and third-party tools like Kafka. Understand data querying with Spark SQL and master SQL schema processing. Apply machine learning techniques with Spark MLlib to real-world scenarios. Explore graph processing and analytics using Spark GraphX. Author(s) None Kumar and None Gulati, experienced professionals in Java development and big data, bring their wealth of practical experience and passion for teaching to this book. With a clear and concise writing style, they aim to simplify Spark for Java developers, making big data approachable. Who is it for? This book is perfect for Java developers who are eager to expand their skillset into big data processing with Apache Spark. Whether you are a seasoned Spark user or first diving into big data concepts, this book meets you at your level. With practical examples and straightforward explanations, you can unlock the potential of Spark in real-world scenarios.