IBM® Storage Virtualize based storage systems are secure storage platforms that implement various security-related features, in terms of system-level access controls and data-level security features. This document outlines the available security features and options of IBM Storage Virtualize based storage systems. It is not intended as a "how to" or best practice document. Instead, it is a checklist of features that can be reviewed by a user security team to aid in the definition of a policy to be followed when implementing IBM FlashSystem®, IBM SAN Volume Controller, and IBM Storage Virtualize for Public Cloud. IBM Storage Virtualize features the following levels of security to protect against threats and to keep the attack surface as small as possible: The first line of defense is to offer strict verification features that stop unauthorized users from using login interfaces and gaining access to the system and its configuration. The second line of defense is to offer least privilege features that restrict the environment and limit any effect if a malicious actor does access the system configuration. The third line of defense is to run in a minimal, locked down, mode to prevent damage spreading to the kernel and rest of the operating system. The fourth line of defense is to protect the data at rest that is stored on the system from theft, loss, or corruption (malicious or accidental). The topics that are discussed in this paper can be broadly split into two categories: System security: This type of security encompasses the first three lines of defense that prevent unauthorized access to the system, protect the logical configuration of the storage system, and restrict what actions users can perform. It also ensures visibility and reporting of system level events that can be used by a Security Information and Event Management (SIEM) solution, such as IBM QRadar®. Data security: This type of security encompasses the fourth line of defense. It protects the data that is stored on the system against theft, loss, or attack. These data security features include Encryption of Data At Rest (EDAR) or IBM Safeguarded Copy (SGC). This document is correct as of IBM Storage Virtualize 8.5.3.

Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse. Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift's underlying mechanisms and options to help you explore out-of-the box automation. Whether you're a data engineer who wants to learn the art of the possible or a DBA looking to take advantage of machine learning-based auto-tuning, this book helps you get the most value from Amazon Redshift. By understanding Amazon Redshift features, you'll achieve excellent analytic performance at the best price, with the least effort. This book helps you: Build a cloud data strategy around Amazon Redshift as foundational data warehouse Get started with Amazon Redshift with simple-to-use data models and design best practices Understand how and when to use Redshift Serverless and Redshift provisioned clusters Take advantage of auto-tuning options inherent in Amazon Redshift and understand manual tuning options Transform your data platform for predictive analytics using Redshift ML and break silos using data sharing Learn best practices for security, monitoring, resilience, and disaster recovery Leverage Amazon Redshift integration with other AWS services to unlock additional value

This book explains how to implement a data lake strategy, covering the technical and business challenges architects commonly face. It also illustrates how and why client requirements should drive architectural decisions. Drawing upon a specific case from his own experience, author Nayanjyoti Paul begins with the consideration from which all subsequent decisions should flow: what does your customer need? He also describes the importance of identifying key stakeholders and the key points to focus on when starting a new project. Next, he takes you through the business and technical requirement-gathering process, and how to translate customer expectations into tangible technical goals. From there, you’ll gain insight into the security model that will allow you to establish security and legal guardrails, as well as different aspects of security from the end user’s perspective. You’ll learn which organizational roles need to be onboarded into the data lake, their responsibilities, the services they need access to, and how the hierarchy of escalations should work. Subsequent chapters explore how to divide your data lakes into zones, organize data for security and access, manage data sensitivity, and techniques used for data obfuscation. Audit and logging capabilities in the data lake are also covered before a deep dive into designing data lakes to handle multiple kinds and file formats and access patterns. The book concludes by focusing on production operationalization and solutions to implement a production setup. After completing this book, you will understand how to implement a data lake, the best practices to employ while doing so, and will be armed with practical tips to solve business problems. What You Will Learn Understand the challenges associated with implementing a data lake Explore the architectural patterns and processes used to design a new data lake Design and implement data lake capabilities Associate business requirements with technical deliverables to drive success Who This Book Is For Data Scientists and Architects, Machine Learning Engineers, and Software Engineers.

The Presto community has mushroomed since its origins at Facebook in 2012. But ramping up this open source distributed SQL query engine can be challenging even for the most experienced engineers. With this practical book, data engineers and architects, platform engineers, cloud engineers, and software engineers will learn how to use Presto operations at your organization to derive insights on datasets wherever they reside. Authors Angelica Lo Duca, Tim Meehan, Vivek Bharathan, and Ying Su explain what Presto is, where it came from, and how it differs from other data warehousing solutions. You'll discover why Facebook, Uber, Alibaba Cloud, Hewlett Packard Enterprise, IBM, Intel, and many more use Presto and how you can quickly deploy Presto in production. With this book, you will: Learn how to install and configure Presto Use Presto with business intelligence tools Understand how to connect Presto to a variety of data sources Extend Presto for real-time business insight Learn how to apply best practices and tuning Get troubleshooting tips for logs, error messages, and more Explore Presto's architectural concepts and usage patterns Understand Presto security and administration

Find out about the latest attack vectors and how LLMs and generative AI present security challenges for teams and how you can mitigate those problems.

AI and hybrid cloud are increasingly influencing IT infrastructure priorities and strategy. AI is becoming mainstream, yet data access, quality, management and privacy challenges remain. Hybrid cloud has become the norm, and 39% of data breaches studied resulted in the loss of data across multiple environments including public cloud, private cloud, and on-prem. As businesses modernize applications, they are looking to innovate and deploy as-a-service in best-fit environments to improve business outcomes. IBM Infrastructure plays an important role for our clients; it's where data, security, performance and flexibility meet mission-critical needs, particularly in regulated industries. Join Hillery Hunter, CTO and General Manager, Innovation for IBM Infrastructure to see our latest solutions and learn how we are helping clients deliver new value with future-ready infrastructure.

Have you ever checked if your app is secure? Do you have any tools that check vulnerabilities? What about user data? Join us and learn about key vectors of attacks and practices to protect your web application.

This IBM® Redpaper publication is a comprehensive guide that covers the IBM Power E1050 server (9043-MRX) that uses the latest IBM Power10 processor-based technology and supports IBM AIX® and Linux operating systems (OSs). The goal of this paper is to provide a hardware architecture analysis and highlight the changes, new technologies, and major features that are being introduced in this system, such as: The latest IBM Power10 processor design, including the dual-chip module (DCM) packaging, which is available in various configurations from 12 - 24 cores per socket. Support of up to 16 TB of memory. Native Peripheral Component Interconnect Express (PCIe) 5th generation (Gen5) connectivity from the processor socket to deliver higher performance and bandwidth for connected adapters. Open Memory Interface (OMI) connected Differential Dual Inline Memory Module (DDIMM) memory cards delivering increased performance, resiliency, and security over industry-standard memory technologies, including transparent memory encryption. Enhanced internal storage performance with the use of native PCIe-connected Non-volatile Memory Express (NVMe) devices in up to 10 internal storage slots to deliver up to 64 TB of high-performance, low-latency storage in a single 4-socket system. Consumption-based pricing in the Power Private Cloud with Shared Utility Capacity commercial model to allow customers to consume resources more flexibly and efficiently, including AIX, Red Hat Enterprise Linux (RHEL), SUSE Linux Enterprise Server, and Red Hat OpenShift Container Platform workloads. This publication is for professionals who want to acquire a better understanding of IBM Power products. The intended audience includes: IBM Power customers Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the set of IBM Power documentation by providing a desktop reference that offers a detailed technical description of the Power E1050 Midrange server model. This paper does not replace the current marketing materials and configuration tools. It is intended as an extra source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions..

Your company relies on data to succeed—data that traditionally comes from a business's transactional processes, pulled from the transaction systems through an extract-transform-load (ETL) process into a warehouse for reporting purposes. But this data flow is no longer sufficient given the growth of the internet of things (IOT), web commerce, and cybersecurity. How can your company keep up with today's increasing magnitude of data and insights? Organizations that can no longer rely on data generated by business processes are looking outside their workflow for information on customer behavior, retail patterns, and industry trends. In this report, author Christopher Gardner examines the challenges of building a framework that provides universal access to data. You will: Learn the advantages and challenges of universal data access, including data diversity, data volume, and the speed of analytic operations Discover how to build a framework for data diversity and universal access Learn common methods for improving database and performance SLAs Examine the organizational requirements that a fast universal data access platform must meet Explore a case study that demonstrates how components work together to form a multiaccess, high-volume, high-performance interface About the author: Christopher Gardner is the campus Tableau application administrator at the University of Michigan, controlling security, updates, and performance maintenance.

David is a Machine Learning Engineer and technologist focused on building embedded systems to use novel techniques, and state of the art technologies (Podman, Balena, TensorFlow, Flutter) in machine learning. Software developer with experience in software exploitation, information security, open-source development and DevOps practices. Community leader for the data science community in Colo…

Roya is a research scientist who is passionate about advancing artificial intelligence technologies. She is particularly interested in computer vision and pattern recognition and has developed machine learning solutions for various applications, including healthcare, assistive technology, and security. Roya is also an advocate for women's rights. She is a Google Women's Techmaker Ambassador.