This IBM® Redpaper™ presents and positions the DS8882F. The DS8882F adds a modular rack-mountable enterprise storage system to the DS8880 family of all-flash enterprise storage systems. The modular system can be integrated into 16U contiguous space of an existing IBM z14™ Model ZR1 (z14 Model ZR1), IBM LinuxONE™ Rockhopper II (z14 Model LR1), or other standard 19-inch wide rack. The DS8882F allows you to take advantage of the performance boost of DS8880 all-flash enterprise systems and advanced features while limiting datacenter footprint and power infrastructure requirements.

Mitigate the risks involved in migrating away from a proprietary database platform toward MariaDB’s open source database engine. This book will help you assess the risks and the work involved, and ensure a successful migration. Migrating to MariaDB describes the process and lessons learned during a migration from a proprietary database management engine to the MariaDB open source solution. The book discusses the drivers for making the decision and change, walking you through all aspects of the process from evaluating the licensing, navigating the pitfalls and hurdles of a migration, through to final implementation on the new platform. The book highlights the cost-effectiveness of MariaDB and how the licensing worries are simplified in comparison to running on a proprietary platform. You’ll learn to do your own risk assessment, to identify database and application code that may need to be modified or re-implemented, and to identify MariaDB features to provide the security and failover protection needed by corporate customers. Let the author’s experience in migrating a financial firm to MariaDB inform your own efforts, helping you to develop a road map for both technical and political success within your own organization as you migrate away from proprietary lock-in toward MariaDB’s open source solution. What You'll Learn Evaluate and compare licensing costs between proprietary databases and MariaDB Perform a proper risk assessment to inform your planning and execution of the migration Build a migration road map from the book’s example that is specific to your situation Make needed application changes and migrate data to the MariaDB open source database engine Who This Book Is For Technical professionals (including database administrators, programmers, and technical management) who are interested in migrating away from a proprietary database platform toward MariaDB’s open source database engine and need to assess the risks and the work involved

This IBM® Redpaper™ publication describes the IBM DS8880 High-Performance Enclosure (HPFE) Gen2 architecture and configuration, as of DS8880 Release 8.51. The DS8880 HPFE Gen2 is a 2U Redundant Array of Independent Disks (RAID) flash enclosure with associated Flash RAID adapters that can be used exclusively with DS8880 models. The flash enclosure and Flash RAID adapters are installed in pairs. Each storage enclosure pair can support 16, 32, or 48 encryption-capable flash drives (2.5-inch, 63.5 mm form factor).

This IBM® Redbooks® Product Guide describes the IBM Storage Networking SAN768C-6. IBM Storage Networking SAN768C-6 has the industry's highest port density for a storage area network (SAN) director and features 768 line-rate 32 gigabits per second (Gbps) or 16 Gbps Fibre Channel ports. Designed to support multiprotocol workloads, IBM Storage Networking SAN768C-6 enables SAN consolidation and collapsed-core solutions for large enterprises, which reduces the number of managed switches and leads to easy-to-manage deployments. IBM Storage Networking SAN768C-6 supports the 48-Port 32 Gbps Fibre Channel Switching Module, the 48-Port 16 Gbps Fibre Channel Switching Module, the 48-port 10 Gbps FCoE Switching Module, the 24-port 40 Gbps FCoE switching module, and the 24/10-port SAN Extension Module. By reducing the number of front-panel ports that are used on inter-switch links (ISLs), it also offers room for future growth. IBM Storage Networking SAN768C-6 addresses the mounting storage requirements of today's large virtualized data centers. As a director-class SAN switch, IBM Storage Networking SAN768C-6 uses the same operating system and management interface as other IBM data center switches. It brings intelligent capabilities to a high-performance, protocol-independent switch fabric, and delivers uncompromising availability, security, scalability, simplified management, and the flexibility to integrate new technologies. You can use IBM Storage Networking SAN768C-6 to transparently deploy unified fabrics with Fibre Channel and Fibre Channel over Ethernet (FCoE) connectivity to achieve low total cost of ownership (TCO). For mission-critical enterprise storage networks that require secure, robust, cost-effective business-continuance services, the FCIP extension module is designed to deliver outstanding SAN extension performance, reducing latency for disk and tape operations with FCIP acceleration features, including FCIP write acceleration and FCIP tape write and read acceleration.

"Bioinformatics with Python Cookbook" offers a detailed exploration into the modern approaches to computational biology using the Python programming language. Through hands-on recipes, you will master the practical applications of bioinformatics, enabling you to analyze vast biological data effectively using Python libraries and tools. What this Book will help me do Master processing and analyzing genomic datasets in Python to enable accurate bioinformatics discoveries. Understand and apply next-generation sequencing techniques for advanced biological research. Learn to utilize machine learning approaches such as PCA and decision trees for insightful data analysis in biology. Gain proficiency in using high-performance computing frameworks like Dask and Spark for scalable bioinformatics workflows. Develop capabilities to visually represent biological data interactions and insights for presentation and analysis. Author(s) Tiago Antao is a computational scientist specializing in bioinformatics with extensive experience in Python programming applied to biological sciences. He has worked on numerous bioinformatics projects and has a special interest in using Python to bridge biology and data science. Tiago's approachable writing style ensures that both newcomers and experts benefit from his insights. Who is it for? This book is designed for bioinformatics professionals, researchers, and data scientists who are eager to harness the power of Python programming for their biological data analysis needs. If you are familiar with Python and are looking to tackle intermediate to advanced bioinformatics challenges using practical recipes, this book is ideal for you. It is suitable for those seeking to expand their knowledge in computational biology and data visualization techniques. Whether you are working on next-generation sequencing or population genetics, this resource will guide you effectively.

This book, Hands-On Big Data Modeling, provides you with practical guidance on data modeling techniques, focusing particularly on the challenges of big data. You will learn the concepts behind various data models, explore tools and platforms for efficient data management, and gain hands-on experience with structured and unstructured data. What this Book will help me do Master the fundamental concepts of big data and its challenges. Explore advanced data modeling techniques using SQL, Python, and R. Design effective models for structured, semi-structured, and unstructured data types. Apply data modeling to real-world datasets like social media and sensor data. Optimize data models for performance and scalability in various big data platforms. Author(s) The authors of this book are experienced data architects and engineers with a strong background in developing scalable data solutions. They bring their collective expertise to simplify complex concepts in big data modeling, ensuring readers can effectively apply these techniques in their projects. Who is it for? This book is intended for data architects, business intelligence professionals, and any programmer interested in understanding and applying big data modeling concepts. If you are already familiar with basic data management principles and want to enhance your skills, this book is perfect for you. You will learn to tackle real-world datasets and create scalable models. Additionally, it is suitable for professionals transitioning to working with big data frameworks.

Dive into "Hands-On Data Science with R" and embark on a journey to master the R language for practical data science applications. This comprehensive guide walks through data manipulation, visualization, and advanced analytics, preparing you to tackle real-world data challenges with confidence. What this Book will help me do Understand how to utilize popular R packages effectively for data science tasks. Learn techniques for cleaning, preprocessing, and exploring datasets. Gain insights into implementing machine learning models in R for predictive analytics. Master the use of advanced visualization tools to extract and communicate insights. Develop expertise in integrating R with big data platforms like Hadoop and Spark. Author(s) This book was written by experts in data science and R including Doug Ortiz and his co-authors. They bring years of industry experience and a desire to teach, presenting complex topics in an approachable manner. Who is it for? Designed for data analysts, statisticians, or programmers with basic R knowledge looking to dive into machine learning and predictive analytics. If you're aiming to enhance your skill set or gain confidence in tackling real-world data problems, this book is an excellent choice.

Dive into the intricate world of geospatial data with "Hands-On Geospatial Analysis with R and QGIS". This book guides readers through managing, analyzing, and visualizing spatial data using the popular tools R and QGIS. Packed with practical examples, it empowers you to effectively handle GIS and remote sensing data in your projects. What this Book will help me do Understand how to install and set up R and QGIS environments for geospatial tasks. Learn the fundamentals of spatial data processing, including management, visualization, and analysis. Create compelling geospatial visualizations using R packages like ggplot2 and tools in QGIS. Master raster data handling and leverage the QGIS graphical modeler for automating geoprocessing tasks. Apply machine learning techniques to geospatial problems such as landslide susceptibility mapping using real-world data. Author(s) None Hamson and None Islam are experts in the field of geospatial analysis and provide practical, actionable insights throughout this book. With extensive experience in GIS and remote sensing technologies, they focus on guiding readers from basic principles to advanced applications. Their collaborative teaching style ensures clarity and accessibility for learners at different skill levels. Who is it for? This book is ideal for geographers, environmental scientists, and other professionals working with spatial data. Beginner to intermediate-level readers will find it approachable, with step-by-step instructions to build their expertise. While prior familiarity with R or QGIS can be helpful, it is not required. The book is tailored for those eager to expand their skills in geospatial data analysis and visualization.

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. What You Will Learn Discover R, statistics, data science, data mining, and big data Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions Work with descriptive statistics Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations.

In "Hands-On Data Science with SQL Server 2017," you will discover how to implement end-to-end data analysis workflows, leveraging SQL Server's robust capabilities. This book guides you through collecting, cleaning, and transforming data, querying for insights, creating compelling visualizations, and even constructing predictive models for sophisticated analytics. What this Book will help me do Grasp the essential data science processes and how SQL Server supports them. Conduct data analysis and create interactive visualizations using Power BI. Build, train, and assess predictive models using SQL Server tools. Integrate SQL Server with R, Python, and Azure for enhanced functionality. Apply best practices for managing and transforming big data with SQL Server. Author(s) Marek Chmel and Vladimír Mužný bring their extensive experience in data science and database management to this book. Marek is a seasoned database specialist with a strong background in SQL, while Vladimír is known for his instructional expertise in analytics and data manipulation. Together, they focus on providing actionable insights and practical examples tailored for data professionals. Who is it for? This book is an ideal resource for aspiring and seasoned data scientists, data analysts, and database professionals aiming to deepen their expertise in SQL Server for data science workflows. Beginners with fundamental SQL knowledge will find it a guided entry into data science applications. It is especially suited for those who aim to implement data-driven solutions in their roles while leveraging SQL's capabilities.

Mastering Matplotlib 2.x guides you through the art and science of creating sophisticated data visualizations with Python's powerful Matplotlib library. You will start by learning the basics of plotting and customizing your charts, progressing to more advanced topics such as 3D visualization, geospatial data display, and creating interactive plots using Jupyter Notebook. What this Book will help me do Create complex and highly customizable data plots using Matplotlib. Effectively visualize data in three dimensions, including geospatial data. Use advanced matplotlib features to represent non-Cartesian and vector data. Build interactive visualizations using Jupyter Notebook and Python. Develop special-purpose and movie-style plots to enhance data representation. Author(s) None Keller is a seasoned software engineer and data visualization enthusiast with years of experience using Python for data analysis. Their practical and hands-on approach ensures that readers can directly apply the concepts taught in their projects. None aims to make advanced visualization techniques accessible to all. Who is it for? This book is perfect for developers, scientists, and analysts who need sophisticated visualization tools for their projects. Prior experience with Python and basic familiarity with Matplotlib will help you get the most out of the book. If you're looking to deepen your understanding of data visualization or to create interactive and advanced visualizations, this book is for you.

PostgreSQL 11 Server Side Programming Quick Start Guide introduces you to the world of database programming directly at the database level. This book delves into the concepts of server-side programming, providing you with the necessary tools to author stored procedures, triggers, and extensions for your PostgreSQL instance. What this Book will help me do Learn how to create stored procedures and functions for efficient database logic. Understand how to use triggers and rules to maintain data integrity. Gain expertise in developing extensions to extend PostgreSQL functionality. Master techniques for handling inter-process communication and background workers. Explore custom data types and integration with programming languages like Java and Perl. Author(s) None Ferrari, a seasoned database administrator and developer, specializes in delivering insightful PostgreSQL training. With extensive experience in both database management and software development, None brings practical knowledge and real-world examples to guide readers through mastering PostgreSQL server-side programming. Who is it for? This book is tailored for database administrators, developers, and engineers who have a basic understanding of PostgreSQL and are looking to expand their knowledge into server-side programming. If you're aiming to implement advanced database functionality or streamline data management tasks in PostgreSQL, this book is for you. It is ideal for those who wish to apply database programming techniques to enterprise-grade challenges. Beginner-friendly but designed to empower professionals with actionable insights.

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Unlock the world of geospatial analysis and mapping with 'Learn QGIS.' This comprehensive guide takes you through the capabilities of QGIS 3.4, covering everything from data loading and styling to spatial analysis and plugin development. Geared towards beginners and seasoned GIS users alike, you'll gain hands-on expertise to master QGIS effectively and confidently. What this Book will help me do Load, edit, and manage geospatial data efficiently in QGIS 3.4 for impactful analysis. Create professional-grade maps with custom styling and data visualization techniques. Delve into the QGIS 3.4 processing toolbox, enhancing analysis workflows. Build bespoke QGIS plugins using Python and QT Designer for tailored solutions. Use QGIS 3.4's advanced features like 3D views and GeoPackage efficiently. Author(s) None Cutts and Anita Graser bring their extensive technical expertise to 'Learn QGIS.' None Cutts has a background in geospatial technologies and a focus on practical GIS applications. Anita Graser is a recognized QGIS expert, experienced in both software development and geospatial analysis. Together, they share their knowledge in an accessible style, ensuring readers of different levels can benefit. Who is it for? This book is ideal for developers, consultants, or GIS enthusiasts who want to expand their skills in using QGIS 3.4 for geospatial data analysis and mapping. Beginners looking to understand core QGIS capabilities will also find value. If you're aiming to develop professional maps and customize QGIS, this is the resource for you.

Abstract Continuing its commitment to developing and delivering industry-leading storage technologies, IBM® introduces Data Reduction Pools (DRP) and Deduplication powered by IBM Spectrum™ Virtualize, which are innovative storage features that deliver essential storage efficiency technologies and exceptional ease of use and performance, all integrated into a proven design. This book discusses Data Reduction Pools (DRP) and Deduplication and is intended for experienced storage administrators who are fully familiar with IBM Spectrum Virtualize, SAN Volume Controller, and the Storwize family of products.

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you’ll get a chance to revisit the concepts you’ve covered so far. What You Will Learn Use Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

This textbook reviews the methodologies of reliability prediction as currently used in industries such as electronics, automotive, aircraft, aerospace, off-highway, farm machinery, and others. It then discusses why these are not successful; and, presents methods developed by the authors for obtaining accurate information for successful prediction. The approach is founded on approaches that accurately duplicate the real world use of the product. Their approach is based on two fundamental components needed for successful reliability prediction; first, the methodology necessary; and, second, use of accelerated reliability and durability testing as a source of the necessary data. Applicable to all areas of engineering, this textbook details the newest techniques and tools to achieve successful reliabilityprediction and testing. It demonstrates practical examples of the implementation of the approaches described. This book is a tool for engineers, managers, researchers, in industry, teachers, and students. The reader will learn the importance of the interactions of the influencing factors and the interconnections of safety and human factors in product prediction and testing.

The Foundational Hands-On Skills You Need to Dive into Data Science “Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills.” –From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Programming Skills for Data Science Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, you'll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything's focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Architect and deploy a Power BI solution. This book will help you understand the many available options and choose the best combination for hosting, developing, sharing, and deploying a Power BI solution within your organization. Pro Power BI Architecture provides detailed examples and explains the different methods available for sharing and securing Power BI content so that only intended recipients can see it. Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and building analysis, methods of sharing analyses that don’t cover all the requirements of your business or organization, and inconsistent security models. The knowledge provided in this book will allow you to choose an architecture and deployment model that suits the needs of your organization, ensuring that you do not spend your time maintaining your solution but onusing it for its intended purpose and gaining business value from mining and analyzing your organization’s data. What You'll Learn Architect and administer enterprise-level Power BI solutions Choose the right sharing method for your Power BI solution Create and manage environments for development, testing, and production Implement row level security in multiple ways to secure your data Save money by choosing the right licensing plan Select a suitable connection type—Live Connection, DirectQuery, or Scheduled Refresh—for your use case Set up a Power BI gateway to bridge between on-premises data sources and the Power BI cloud service Who This Book Is For Data analysts, developers, architects, and managers who want to leverage Power BI for their reporting solution

This third edition of Paul Murrell’s classic book on using R for graphics represents a major update, with a complete overhaul in focus and scope. It focuses primarily on the two core graphics packages in R - graphics and grid - and has a new section on integrating graphics.