IBM Spectrum® 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 Spectrum 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 Spectrum Virtualize for Public Cloud. 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, but are not limited to, encryption of data at rest (EDAR) or IBM Safeguarded Copy (SGC). This document is correct as of IBM Spectrum Virtualize version 8.5.0.

Today, more than 80% of the data transmitted over networks and archived on our computers, tablets, cell phones or clouds is multimedia data - images, videos, audio, 3D data. The applications of this data range from video games to healthcare, and include computer-aided design, video surveillance and biometrics. It is becoming increasingly urgent to secure this data, not only during transmission and archiving, but also during its retrieval and use. Indeed, in today’s "all-digital" world, it is becoming ever-easier to copy data, view it unrightfully, steal it or falsify it. Multimedia Security 1 analyzes the issues of the authentication of multimedia data, code and the embedding of hidden data, both from the point of view of defense and attack. Regarding the embedding of hidden data, it also covers invisibility, color, tracing and 3D data, as well as the detection of hidden messages in an image by steganalysis.

"Getting Started with CockroachDB" provides an in-depth introduction to CockroachDB, a modern, distributed SQL database designed for cloud-native applications. Through this guide, you'll learn how to deploy, manage, and optimize CockroachDB to build highly reliable, scalable database solutions tailored for demanding and distributed workloads. What this Book will help me do Understand the architecture and design principles of CockroachDB and its fault-tolerant model. Learn how to set up and manage CockroachDB clusters for high availability and automatic scaling. Discover the concepts of data distribution and geo-partitioning to achieve low-latency global interactions. Explore indexing mechanisms in CockroachDB to optimize query performance for fast data retrieval. Master operational strategies, security configuration, and troubleshooting techniques for database management. Author(s) Kishen Das Kondabagilu Rajanna is an experienced software developer and database expert with a deep interest in distributed architectures. With hands-on experience working with CockroachDB and other database technologies, Kishen is passionate about sharing actionable insights with readers. His approach focuses on equipping developers with practical skills to excel in building and managing scalable, efficient database services. Who is it for? This book is ideal for software developers, database administrators, and database engineers seeking to learn CockroachDB for building robust, scalable database systems. If you're new to CockroachDB but possess basic database knowledge, this guide will equip you with the practical skills to leverage CockroachDB's capabilities effectively.

Enterprise networks are large and rely on numerous connected endpoints to ensure smooth operational efficiency. However, they also present a challenge from a security perspective. The focus of this Blueprint is to demonstrate an early threat detection against the network fabric that is powered by Brocade that uses IBM® QRadar®. It also protects the same if a cyberattack or an internal threat by rouge user within the organization occurs. The publication also describes how to configure the syslog that is forwarding on Brocade SAN FOS. Finally, it explains how the forwarded audit events are used for detecting the threat and runs the custom action to mitigate the threat. The focus of this publication is to proactively start a cyber resilience workflow from IBM QRadar to block an IP address when multiple failed logins on Brocade switch are detected. As part of early threat detection, a sample rule that us used by IBM QRadar is shown. A Python script that also is used as a response to block the user's IP address in the switch is provided. Customers are encouraged to create control path or data path use cases, customized IBM QRadar rules, and custom response scripts that are best-suited to their environment. The use cases, QRadar rules, and Python script that are presented here are templates only and cannot be used as-is in an environment.

Understand the different access control paradigms available in the Snowflake Data Cloud and learn how to implement access control in support of data privacy and compliance with regulations such as GDPR, APPI, CCPA, and SOX. The information in this book will help you and your organization adhere to privacy requirements that are important to consumers and becoming codified in the law. You will learn to protect your valuable data from those who should not see it while making it accessible to the analysts whom you trust to mine the data and create business value for your organization. Snowflake is increasingly the choice for companies looking to move to a data warehousing solution, and security is an increasing concern due to recent high-profile attacks. This book shows how to use Snowflake's wide range of features that support access control, making it easier to protect data access from the data origination point all the way to the presentation and visualization layer.Reading this book helps you embrace the benefits of securing data and provide valuable support for data analysis while also protecting the rights and privacy of the consumers and customers with whom you do business. What You Will Learn Identify data that is sensitive and should be restricted Implement access control in the Snowflake Data Cloud Choose the right access control paradigm for your organization Comply with CCPA, GDPR, SOX, APPI, and similar privacy regulations Take advantage of recognized best practices for role-based access control Prevent upstream and downstream services from subverting your access control Benefit from access control features unique to the Snowflake Data Cloud Who This Book Is For Data engineers, database administrators, and engineering managers who wantto improve their access control model; those whose access control model is not meeting privacy and regulatory requirements; those new to Snowflake who want to benefit from access control features that are unique to the platform; technology leaders in organizations that have just gone public and are now required to conform to SOX reporting requirements

Design for large-scale, high-performance queries using Snowflake’s query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis. Mastering Snowflake Solutions starts with a refresher on Snowflake’s unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater operational agility in responding to the needs of modern enterprises, for example in supporting agile development techniques via database cloning. The practical examples and in-depth background on theory in this book help you unleash the power of Snowflake in building a high-performance system with little to no administrative overhead. Your result from reading will be a deep understanding of Snowflake that enables taking full advantage of Snowflake’s architecture to deliver value analytics insight to your business. What You Will Learn Optimize performance and costs associated with your use of the Snowflake data platform Enable data security to help in complying with consumer privacy regulations such as CCPA and GDPR Share data securely both inside your organization and with external partners Gain visibility to each interaction with your customersusing continuous data feeds from Snowpipe Break down data silos to gain complete visibility your business-critical processes Transform customer experience and product quality through real-time analytics Who This Book Is for Data engineers, scientists, and architects who have had some exposure to the Snowflake data platform or bring some experience from working with another relational database. This book is for those beginning to struggle with new challenges as their Snowflake environment begins to mature, becoming more complex with ever increasing amounts of data, users, and requirements. New problems require a new approach and this book aims to arm you with the practical knowledge required to take advantage of Snowflake’s unique architecture to get the results you need.

Globally available resources have become the status quo. They're accessible, distributed, and resilient. Our traditional SQL database options haven't kept up. Centralized SQL databases, even those with read replicas in the cloud, put all the transactional load on a central system. The further away that a transaction happens from the user, the more the user experience suffers. If the transactional data powering the application is greatly slowed down, fast-loading web pages mean nothing. In this report, Paul Modderman, Jim Walker, and Charles Custer explain how distributed SQL fits all applications and eliminates complex challenges like sharding from traditional RDBMS systems. You'll learn how distributed SQL databases can reach global scale without introducing the consistency trade-offs found in NoSQL solutions. These databases come to life through cloud computing, while legacy databases simply can't rise to meet the elastic and ubiquitous new paradigm. You'll learn: Key concepts driving this new technology, including the CAP theorem, the Raft consensus algorithm, multiversion concurrency control, and Google Spanner How distributed SQL databases meet enterprise requirements, including management, security, integration, and Everything as a Service (XaaS) The impact that distributed SQL has already made in the telecom, retail, and gaming industries Why serverless computing is an ideal fit for distributed SQL How distributed SQL can help you expand your company's strategic plan

Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits. In Data Privacy you will learn how to: Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Asset Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and board Data Privacy teaches you to design, develop, and measure the effectiveness of privacy programs. You’ll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen privacy at Google, Netflix, and Uber. The terminology and legal requirements of privacy are all explained in clear, jargon-free language. The book’s constant awareness of business requirements will help you balance trade-offs, and ensure your user’s privacy can be improved without spiraling time and resource costs. About the Technology Data privacy is essential for any business. Data breaches, vague policies, and poor communication all erode a user’s trust in your applications. You may also face substantial legal consequences for failing to protect user data. Fortunately, there are clear practices and guidelines to keep your data secure and your users happy. About the Book Data Privacy: A runbook for engineers teaches you how to navigate the trade-offs between strict data security and real world business needs. In this practical book, you’ll learn how to design and implement privacy programs that are easy to scale and automate. There’s no bureaucratic process—just workable solutions and smart repurposing of existing security tools to help set and achieve your privacy goals. What's Inside Classify data based on privacy risk Set up capabilities for data export that meet legal requirements Establish a review process to accelerate privacy impact assessment Design a consent management platform to capture user consent About the Reader For engineers and business leaders looking to deliver better privacy. About the Author Nishant Bhajaria leads the Technical Privacy and Strategy teams for Uber. His previous roles include head of privacy engineering at Netflix, and data security and privacy at Google. Quotes I wish I had had this text in 2015 or 2016 at Netflix, and it would have been very helpful in 2008–2012 in a time of significant architectural evolution of our technology. - From the Foreword by Neil Hunt, Former CPO, Netflix Your guide to building privacy into the fabric of your organization. - John Tyler, JPMorgan Chase The most comprehensive resource you can find about privacy. - Diego Casella, InvestSuite Offers some valuable insights and direction for enterprises looking to improve the privacy of their data. - Peter White, Charles Sturt University

IBM® Hyper Protect Data Controller is designed to provide privacy protection of your sensitive data and give ease of control and auditability. It can manage how data is shared securely through a central control. Hyper Protect Data Controller can protect data wherever it goes—security policies are kept and honored whenever the data is accessed and future data access can be revoked even after data leaves the system of record. This IBM Redbooks® publication can assist you with determining how to get started with IBM Hyper Protect Data Controller through a use case approach. It will help you plan for, install, tailor and configure the Hyper Protect Data Controller. It includes information about the following topics: Concepts and reference architecture Common use cases with implementation guidance and advice Implementation and policy examples Typical operational tasks for creating policies and preparing for audits Monitoring user activity and events This IBM Redbooks publication is written for IT Managers, IT Architects, Security Administrators, data owners, and data consumers.

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

Understand the essentials of the Snowflake Database and the overall Snowflake Data Cloud. This book covers how Snowflake’s architecture is different from prior on-premises and cloud databases. The authors also discuss, from an insider perspective, how Snowflake grew so fast to become the largest software IPO of all time. Snowflake was the first database made specifically to be optimized with a cloud architecture. This book helps you get started using Snowflake by first understanding its architecture and what separates it from other database platforms you may have used. You will learn about setting up users and accounts, and then creating database objects. You will know how to load data into Snowflake and query and analyze that data, including unstructured data such as data in XML and JSON formats. You will also learn about Snowflake’s compute platform and the different data sharing options that are available. What YouWill Learn Run analytics in the Snowflake Data Cloud Create users and roles in Snowflake Set up security in Snowflake Set up resource monitors in Snowflake Set up and optimize Snowflake Compute Load, unload, and query structured and unstructured data (JSON, XML) within Snowflake Use Snowflake Data Sharing to share data Set up a Snowflake Data Exchange Use the Snowflake Data Marketplace Who This Book Is For Database professionals or information technology professionals who want to move beyond traditional database technologies by learning Snowflake, a new and massively scalable cloud-based database solution

Apply different enterprise integration and processing strategies available with Pulsar, Apache's multi-tenant, high-performance, cloud-native messaging and streaming platform. This book is a comprehensive guide that examines using Pulsar Java libraries to build distributed applications with message-driven architecture. You'll begin with an introduction to Apache Pulsar architecture. The first few chapters build a foundation of message-driven architecture. Next, you'll perform a setup of all the required Pulsar components. The book also covers work with Apache Pulsar client library to build producers and consumers for the discussed patterns. You'll then explore the transformation, filter, resiliency, and tracing capabilities available with Pulsar. Moving forward, the book will discuss best practices when building message schemas and demonstrate integration patterns using microservices. Security is an important aspect of any application;the book will cover authentication and authorization in Apache Pulsar such as Transport Layer Security (TLS), OAuth 2.0, and JSON Web Token (JWT). The final chapters will cover Apache Pulsar deployment in Kubernetes. You'll build microservices and serverless components such as AWS Lambda integrated with Apache Pulsar on Kubernetes. After completing the book, you'll be able to comfortably work with the large set of out-of-the-box integration options offered by Apache Pulsar. What You'll Learn Examine the important Apache Pulsar components Build applications using Apache Pulsar client libraries Use Apache Pulsar effectively with microservices Deploy Apache Pulsar to the cloud Who This Book Is For Cloud architects and software developers who build systems in the cloud-native technologies.

How can you realize MySQL's full power? With High Performance MySQL, you'll learn advanced techniques for everything from setting service-level objectives to designing schemas, indexes, and queries to tuning your server, operating system, and hardware to achieve your platform's full potential. This guide also teaches database administrators safe and practical ways to scale applications through replication, load balancing, high availability, and failover. Updated to reflect recent advances in cloud- and self-hosted MySQL, InnoDB performance, and new features and tools, this revised edition helps you design a relational data platform that will scale with your business. You'll learn best practices for database security along with hard-earned lessons in both performance and database stability. Dive into MySQL's architecture, including key facts about its storage engines Learn how server configuration works with your hardware and deployment choices Make query performance part of your software delivery process Examine enhancements to MySQL's replication and high availability Compare different MySQL offerings in managed cloud environments Explore MySQL's full stack optimization from application-side configuration to server tuning Turn traditional database management tasks into automated processes

Every enterprise application creates data, whether it consists of log messages, metrics, user activity, or outgoing messages. Moving all this data is just as important as the data itself. With this updated edition, application architects, developers, and production engineers new to the Kafka streaming platform will learn how to handle data in motion. Additional chapters cover Kafka's AdminClient API, transactions, new security features, and tooling changes. Engineers from Confluent and LinkedIn responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream processing applications with this platform. Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. You'll examine: Best practices for deploying and configuring Kafka Kafka producers and consumers for writing and reading messages Patterns and use-case requirements to ensure reliable data delivery Best practices for building data pipelines and applications with Kafka How to perform monitoring, tuning, and maintenance tasks with Kafka in production The most critical metrics among Kafka's operational measurements Kafka's delivery capabilities for stream processing systems

This book is your complete guide to Snowflake security, covering account security, authentication, data access control, logging and monitoring, and more. It will help you make sure that you are using the security controls in a right way, are on top of access control, and making the most of the security features in Snowflake. Snowflake is the fastest growing cloud data warehouse in the world, and having the right methodology to protect the data is important both to data engineers and security teams. It allows for faster data enablement for organizations, as well as reducing security risks, meeting compliance requirements, and solving data privacy challenges. There are currently tens of thousands of people who are either data engineers/data ops in Snowflake-using organizations, or security people in such organizations. This book provides guidance when you want to apply certain capabilities, such as data masking, row-level security, column-level security, tackling rolehierarchy, building monitoring dashboards, etc., to your organizations. What You Will Learn Implement security best practices for Snowflake Set up user provisioning, MFA, OAuth, and SSO Set up a Snowflake security model Design roles architecture Use advanced access control such as row-based security and dynamic masking Audit and monitor your Snowflake Data Cloud Who This Book Is For Data engineers, data privacy professionals, and security teams either with security knowledge (preferably some data security knowledge) or with data engineering knowledge; in other words, either “Snowflake people” or “data people” who want to get security right, or “security people” who want to make sure that Snowflake gets handled right in terms of security

Azure Databricks is a robust analytics platform that leverages Apache Spark and seamlessly integrates with Azure services. In the Azure Databricks Cookbook, you'll find hands-on recipes to ingest data, build modern data pipelines, and perform real-time analytics while learning to optimize and secure your solutions. What this Book will help me do Design advanced data workflows integrating Azure Synapse, Cosmos DB, and streaming sources with Databricks. Gain proficiency in using Delta Tables and Spark for efficient data storage and analysis. Learn to create, deploy, and manage real-time dashboards with Databricks SQL. Master CI/CD pipelines for automating deployments of Databricks solutions. Understand security best practices for restricting access and monitoring Azure Databricks. Author(s) None Raj and None Jaiswal are experienced professionals in the field of big data and analytics. They are well-versed in implementing Azure Databricks solutions for real-world problems. Their collaborative writing approach ensures clarity and practical focus. Who is it for? This book is tailored for data engineers, scientists, and big data professionals who want to apply Azure Databricks and Apache Spark to their analytics workflows. A basic familiarity with Spark and Azure is recommended to make the best use of the recipes provided. If you're looking to scale and optimize your analytics pipelines, this book is for you.

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.

Get a comprehensive overview on how to set up and design an effective database with MySQL. This thoroughly updated edition covers MySQL's latest version, including its most important aspects. Whether you're deploying an environment, troubleshooting an issue, or engaging in disaster recovery, this practical guide provides the insights and tools necessary to take full advantage of this powerful RDBMS. Authors Vinicius Grippa and Sergey Kuzmichev from Percona show developers and DBAs methods for minimizing costs and maximizing availability and performance. You'll learn how to perform basic and advanced querying, monitoring and troubleshooting, database management and security, backup and recovery, and tuning for improved efficiency. This edition includes new chapters on high availability, load balancing, and using MySQL in the cloud. Get started with MySQL and learn how to use it in production Deploy MySQL databases on bare metal, on virtual machines, and in the cloud Design database infrastructures Code highly efficient queries Monitor and troubleshoot MySQL databases Execute efficient backup and restore operations Optimize database costs in the cloud Understand database concepts, especially those pertaining to MySQL

Single-purpose databases were designed to address specific problems and use cases. Given this narrow focus, there are inherent tradeoffs required when trying to accommodate multiple datatypes or workloads in your enterprise environment. The result is data fragmentation that spills over into application development, IT operations, data security, system scalability, and availability. In this report, author Alice LaPlante explains why developing modern, data-driven applications may be easier and more synergistic when using a converged database. Senior developers, architects, and technical decision-makers will learn cloud-native application development techniques for working with both structured and unstructured data. You'll discover ways to run transactional and analytical workloads on a single, unified data platform. This report covers: Benefits and challenges of using a converged database to develop data-driven applications How to use one platform to work with both structured and unstructured data that includes JSON, XML, text and files, spatial and graph, Blockchain, IoT, time series, and relational data Modern development practices on a converged database, including API-driven development, containers, microservices, and event streaming Use case examples including online food delivery, real-time fraud detection, and marketing based on real-time analytics and geospatial targeting

DESIGNING BIG DATA PLATFORMS Provides expert guidance and valuable insights on getting the most out of Big Data systems An array of tools are currently available for managing and processing data—some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. Designing Big Data Platforms provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems. This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems Highlights and explains how data is processed at scale Includes an introduction to the foundation of a modern data platform Designing Big Data Platforms: How to Use, Deploy, and Maintain Big Data Systems is a must-have for all professionals working with Big Data, as well researchers and students in computer science and related fields.