Informatyka
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Abhilash G B
This is a step-by-step guide that will help you understand disaster recovery using VMware vSphere Replication 5.5 and VMware vCenter Site Recovery Manager (SRM) 5.5. The topics and configuration procedures are accompanied with relevant screenshots, flowcharts, and logical diagrams that makes grasping the concepts easier. This book is a guide for anyone who is keen on using vSphere Replication or vCenter Site Recovery Manager as a disaster recovery solution. This is an excellent handbook for solution architects, administrators, on-field engineers, and support professionals. Although the book assumes that the reader has some basic knowledge of data center virtualization using VMware vSphere, it can still be a very good reference for anyone who is new to virtualization.
Nelson Enriquez, Samundar Singh Rathore
Business Intelligence is a type of technology that has been proven to support business decisions in an organization. MicroStrategy 9 is a fully-integrated BI platform that makes Business Intelligence faster, easier, and more user-friendly. It enables businesses to generate their own reports and dashboards without the need for technical knowledge.This practical, hands-on guide will provide Business Intelligence for executives, as well as enable BI reports and dashboards without the dependency of IT savvy personnel. It will allow you to design, build, and share business relevant data in hours, in a secure way, including mobile devices and show you how to leverage your transactional information.This example-oriented book looks at the value proposition of cloud computing and the MicroStrategy platform, and features practical exercises for BI reports and dashboard enablement, including the design phase and best practices for when we design a BI report.The book begins with an exploration of MicroStrategy along with typical business needs. Our focus then shifts to best practices for BI reports and dashboard definitions from the functional stand point, with easy-to-do exercises that will allow you to enable the reports in the platform. You will learn about scorecards and dashboards, along with sharing the reports. Next, you will get acquainted with cloud-based services provided by the MicroStrategy platform. By the end of this book, you will able to design, enable, and share BI reports and dashboards without the need for comprehensive technical knowledge, and leverage the latest technology on the market.
Fuheng Wu
As AI models grow to billions and trillions of parameters, distributed systems are essential for training and serving them. Many resources cover fragments of this domain, but none provide a full path from distributed training to inference and production deployment. This book fills that gap with practical, production-focused examples.It starts with GPU and memory estimation, data preparation, and an overview of GPU architecture, interconnects, and core parallelism strategies. You’ll learn training techniques including data parallelism for single and multi-node setups, parameter sharding for memory-efficient scaling, and methods to reduce memory usage in large models.The next section covers distributed inference and deployment. You’ll build high-performance systems using optimized attention, caching, operator fusion, and router-based designs. You’ll deploy on schedulers and container platforms with GPU-aware orchestration and assemble production stacks emphasizing reliability, scalability, and observability.The final section covers benchmarking, performance tuning, and trends like MoE models, edge - cloud co-ordination, and advanced parallelism. Each chapter includes tested code and debugging guidance.By the end, you’ll be able to build distributed AI systems that scale from a single GPU to large clusters.
Distributed Computing in Java 9. Leverage the latest features of Java 9 for distributed computing
Raja Malleswara Rao Malleswara Rao Pattamsetti
Distributed computing is the concept with which a bigger computation process is accomplished by splitting it into multiple smaller logical activities and performed by diverse systems, resulting in maximized performance in lower infrastructure investment. This book will teach you how to improve the performance of traditional applications through the usage of parallelism and optimized resource utilization in Java 9.After a brief introduction to the fundamentals of distributed and parallel computing, the book moves on to explain different ways of communicating with remote systems/objects in a distributed architecture. You will learn about asynchronous messaging with enterprise integration and related patterns, and how to handle large amount of data using HPC and implement distributed computing for databases. Moving on, it explains how to deploy distributed applications on different cloud platforms and self-contained application development. You will also learn about big data technologies and understand how they contribute to distributed computing. The book concludes with the detailed coverage of testing, debugging, troubleshooting, and security aspects of distributed applications so the programs you build are robust, efficient, and secure.
Distributed Computing with Go. Practical concurrency and parallelism for Go applications
V.N. Nikhil Anurag, Jinzhu Zhang, Pankaj Khairnar
Distributed Computing with Go gives developers with a good idea how basic Go development works the tools to fulfill the true potential of Golang development in a world of concurrent web and cloud applications. Nikhil starts out by setting up a professional Go development environment. Then you’ll learn the basic concepts and practices of Golang concurrent and parallel development. You’ll find out in the new few chapters how to balance resources and data with REST and standard web approaches while keeping concurrency in mind. Most Go applications these days will run in a data center or on the cloud, which is a condition upon which the next chapter depends. There, you’ll expand your skills considerably by writing a distributed document indexing system during the next two chapters. This system has to balance a large corpus of documents with considerable analytical demands. Another use case is the way in which a web application written in Go can be consciously redesigned to take distributed features into account. The chapter is rather interesting for Go developers who have to migrate existing Go applications to computationally and memory-intensive environments. The final chapter relates to the rather onerous task of testing parallel and distributed applications, something that is not usually taught in standard computer science curricula.
Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines
Alan Bernardo Palacio
Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Ayman Hourieh
Django is a high-level Python web framework that was developed by a fast-moving online-news operation to meet the stringent twin challenges of newsroom deadlines and the needs of web developers. It is designed to promote rapid development and clean, pragmatic design and lets you build high-performing, elegant web applications rapidly. Django focuses on automating as much as possible and adhering to the DRY (Don't Repeat Yourself) principle, making it easier to build high-performance web applications faster, with less code.This book will show you how to assemble Django's features and take advantage of its power to design, develop, and deploy a fully-featured web site. It will walk you through the creation of an example web application, with lots of code examples.Specially revised for version 1.0 of Django, the book starts by introducing the main design concepts in Django. Next, it leads you through the process of installing Django on your system. After that, you will start right away on building your social bookmarking application using Django. Various Django 1.0 components and sub-frameworks will be explained during this process, and you will learn about them by example.In each chapter, you will build one or more of the features that are essential in Web 2.0 applications, like user management, tags, and AJAX. You will also learn about good software development practices, such as keeping your application secure, and automating testing with unit tests. By the end of the book, you will have built a fully functional real-life Web 2.0 application, and learned how to deploy it to a production server.
Django 1.1 Testing and Debugging. Building rigorously tested and bug-free Django applications
Karen Tracey
Bugs are a time consuming burden during software development. Django's built-in test framework and debugging support help lessen this burden. This book will teach you quick and efficient techniques for using Django and Python tools to eradicate bugs and ensure your Django application works correctly. This book will walk you step by step through development of a complete sample Django application. You will learn how best to test and debug models, views, URL configuration, templates, and template tags. This book will help you integrate with and make use of the rich external environment of test and debugging tools for Python and Django applications. The book starts with a basic overview of testing. It will highlight areas to look out for while testing. You will learn about different kinds of tests available, and the pros and cons of each, and also details of test extensions provided by Django that simplify the task of testing Django applications. You will see an illustration of how external tools that provide even more sophisticated testing features can be integrated into Django's framework. On the debugging front, the book illustrates how to interpret the extensive debugging information provided by Django's debug error pages, and how to utilize logging and other external tools to learn what code is doing.