Dell VxRail System Design and Best Practices. A complete guide to VxRail appliance design and best practices

Victor Wu

Virtualized systems are well established now, and their disparate components can be found bundled together in hyper-converged infrastructures, such as VxRail from Dell EMC. Dell VxRail System Design and Best Practices will take you, as a system architect or administrator, through the process of designing and protecting VxRail systems.While this book assumes a certain level of knowledge of VMware, vSphere 7.x, and vCenter Server, you’ll get a thorough overview of VxRail's components, features, and architecture, as well as a breakdown of the benefits of this hyper-converged system. This guide will give you an in-depth understanding of VxRail, as well as plenty of practical examples and self-assessment questions along the way to help you plan and design every core component of a VxRail system – from vSAN storage policies to cluster expansion. It's no good having a great system if you lose everything when it breaks, so you'll spend some time examining advanced recovery options, such as VMware Site Recovery Manager and Veeam Backup and Replication.By the end of this book, you will have got to grips with Dell’s hyper-converged VxRail offering, taking your virtualization proficiency to the next level.

Practical Data Analysis Cookbook. Over 60 practical recipes on data exploration and analysis

Tomasz Drabas

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors.This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews.By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.

Mastering Parallel Programming with R. Master the robust features of R parallel programming to accelerate your data science computations

Simon R. Chapple, Terence Sloan, Thorsten Forster, Eilidh Troup

R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources.Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R’s built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems.

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Apache Roller 4.0 - Beginner's Guide. A comprehensive, step-by-step guide on how to set up, customize, and market your blog using Apache Roller

Alfonso V. Romero, Brian Fitzpatrick, Alfonso Vidal Romero

Apache Roller enables you to build a fully-featured, multi-user blog server apt for all kinds of blogging sites. It is an ideal tool to create your own blogging network with unlimited users and blogs, forums, photo galleries, and more! While it is exciting to have a list of interesting features it can offer you, it might be a little difficult to get started with it by your self.This book will teach you how to get started with Apache Roller and make the most of all its features using step-by-step, detailed instructions. You will learn how to establish your internet presence with an Apache Roller blog and use the latest web tools to enhance your posts and attract visitors. You will also learn how to promote your blog on popular social bookmarking services and customize it to suit your need.This hands-on and practical book introduces you to Apache Roller. Starting off with the configuration and installation of your own blog, you'll then quickly learn how to add interesting content to your blog with the help of plenty of examples. You'll also learn how to change your blog's visual appearance with the help of Roller themes and templates and how to create a community of blogs for you and your colleagues or friends in your Apache Roller blog server. The book also looks at ways you can manage your community, and keep your site safe and secure, ensuring that it is a spam-free, enjoyable community for your users.

Software Architecture with C# 9 and .NET 5. Architecting software solutions using microservices, DevOps, and design patterns for Azure - Second Edition

Gabriel Baptista, Francesco Abbruzzese

Software architecture is the practice of implementing structures and systems that streamline the software development process and improve the quality of an app. This fully revised and expanded second edition, featuring the latest features of .NET 5 and C# 9, enables you to acquire the key skills, knowledge, and best practices required to become an effective software architect.This second edition features additional explanation of the principles of Software architecture, including new chapters on Azure Service Fabric, Kubernetes, and Blazor. It also includes more discussion on security, microservices, and DevOps, including GitHub deployments for the software development cycle.You will begin by understanding how to transform user requirements into architectural needs and exploring the differences between functional and non-functional requirements. Next, you will explore how to carefully choose a cloud solution for your infrastructure, along with the factors that will help you manage your app in a cloud-based environment.Finally, you will discover software design patterns and various software approaches that will allow you to solve common problems faced during development.By the end of this book, you will be able to build and deliver highly scalable enterprise-ready apps that meet your organization’s business requirements.

Learn Ethereum. A practical guide to help developers set up and run decentralized applications with Ethereum 2.0 - Second Edition

Xun (Brian) Wu, Zhihong Zou, Dongying Song

Ethereum is a blockchain-based, decentralized computing platform that allows you to run smart contracts. With this book, you’ll discover the latest Ethereum tools, frameworks, wallets, and layer 2, along with setting up and running decentralized applications for the complete, end-to-end development experience.Learn Ethereum, 2nd Edition is a comprehensive overview of the Ethereum ecosystem, exploring its concepts, mechanisms, and decentralized application development process. You’ll delve into Ethereum's internals, technologies, and tools, including Ethereum 2.0 and the Ethereum Virtual Machine (EVM), gas, and its account systems. You’ll also explore Ethereum's transition to proof of stake, L1/L2 scaling solutions, DeFi protocols, and the current marketplace. Additionally, you’ll learn about EVM-compatible blockchains, connectivity techniques, and advanced topics such as sharding, off-chain scaling, DAOs, Metaverse, and NFTs.By the end of this book, you’ll be well-equipped to write smart contracts and develop, test, and deploy DApps using various tools, wallets, and frameworks.

Practical Data Science with Python. Learn tools and techniques from hands-on examples to extract insights from data

Nathan George

Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.