Programowanie
Digitalizacja w systemach automatyki SIMATIC. Teoria, przykłady, ćwiczenia
Artur Nowocień
Z pamięci papieru do pamięci komputera Współczesnym przemysłem rządzi... informatyka. Ta dziedzina stale się rozwija i zagarnia pod swoje skrzydła kolejne sektory ― od produkcji, przez logistykę i księgowość, po dystrybucję i sprzedaż. Tyle teorii. W praktyce zaś często się okazuje, że podczas gdy otoczenie biznesowe i technologie pędzą naprzód, systemy stosowane w przemyśle zostają nieco z tyłu. Głównym celem, jaki przyświeca autorowi tej publikacji, skierowanej przede wszystkim do automatyków i programistów sterowników PLC, jest odczarowanie pojęcia digitalizacji i udowodnienie, że technologie, które się w nie wpisują, nie są wcale zarezerwowane dla specjalistów IT. W rzeczywistości wszyscy stosujemy je na co dzień, tylko w okrojonej formie. W książce poruszane są takie tematy jak podstawowe założenia czwartej rewolucji przemysłowej, cyberbezpieczeństwo, mechanizmy informatyczne implementowane na poziomie konwencjonalnych urządzeń automatyki, internet rzeczy, chmury obliczeniowe, systemy brzegowe, a także technologie, które wyznaczają przyszłość automatyki przemysłowej. Każdy rozdział składa się z dwóch części: teoretycznej, zawierającej omówienie podstawowych zagadnień, które należy przyswoić, aby móc świadomie korzystać z danej technologii, i praktycznej, prezentującej jej implementację przy użyciu powszechnie stosowanych komponentów automatyki.
Digitalizacja w systemach automatyki SIMATIC. Teoria, przykłady, ćwiczenia. Wydanie II
Artur Nowocień
Z pamięci papieru do pamięci komputera Współczesnym przemysłem rządzi... informatyka. Ta dziedzina stale się rozwija i zagarnia pod swoje skrzydła kolejne sektory ― od produkcji, przez logistykę i księgowość, po dystrybucję i sprzedaż. Tyle teorii. W praktyce zaś często się okazuje, że podczas gdy otoczenie biznesowe i technologie pędzą naprzód, systemy stosowane w przemyśle zostają nieco z tyłu. Głównym celem, jaki przyświeca autorowi tej publikacji, skierowanej przede wszystkim do automatyków i programistów sterowników PLC, jest odczarowanie pojęcia digitalizacji i udowodnienie, że technologie, które się w nie wpisują, nie są wcale zarezerwowane dla specjalistów IT. W rzeczywistości wszyscy stosujemy je na co dzień, tylko w okrojonej formie. W książce poruszane są takie tematy jak podstawowe założenia czwartej rewolucji przemysłowej, cyberbezpieczeństwo, mechanizmy informatyczne implementowane na poziomie konwencjonalnych urządzeń automatyki, internet rzeczy, chmury obliczeniowe, systemy brzegowe, a także technologie, które wyznaczają przyszłość automatyki przemysłowej. Każdy rozdział składa się z dwóch części: teoretycznej, zawierającej omówienie podstawowych zagadnień, które należy przyswoić, aby móc świadomie korzystać z danej technologii, i praktycznej, prezentującej jej implementację przy użyciu powszechnie stosowanych komponentów automatyki.
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.
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 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.
Francesco Pierfederici
CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
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.
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.
Guanhua Wang
Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.