Cloud Computing A Hands On Approach

Author: Arshdeep Bahga
Publisher: CreateSpace Independent Publishing Platform
ISBN: 1494435144
Format: PDF, Mobi
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About the Book Recent industry surveys expect the cloud computing services market to be in excess of $20 billion and cloud computing jobs to be in excess of 10 million worldwide in 2014 alone. In addition, since a majority of existing information technology (IT) jobs is focused on maintaining legacy in-house systems, the demand for these kinds of jobs is likely to drop rapidly if cloud computing continues to take hold of the industry. However, there are very few educational options available in the area of cloud computing beyond vendor-specific training by cloud providers themselves. Cloud computing courses have not found their way (yet) into mainstream college curricula. This book is written as a textbook on cloud computing for educational programs at colleges. It can also be used by cloud service providers who may be interested in offering a broader perspective of cloud computing to accompany their own customer and employee training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. We have tried to write a comprehensive book that transfers knowledge through an immersive "hands-on approach", where the reader is provided the necessary guidance and knowledge to develop working code for real-world cloud applications. Additional support is available at the book's website: www.cloudcomputingbook.info Organization The book is organized into three main parts. Part I covers technologies that form the foundations of cloud computing. These include topics such as virtualization, load balancing, scalability & elasticity, deployment, and replication. Part II introduces the reader to the design & programming aspects of cloud computing. Case studies on design and implementation of several cloud applications in the areas such as image processing, live streaming and social networks analytics are provided. Part III introduces the reader to specialized aspects of cloud computing including cloud application benchmarking, cloud security, multimedia applications and big data analytics. Case studies in areas such as IT, healthcare, transportation, networking and education are provided.

Laboratory Training Guide

Author: Arshdeep Bahga
Publisher: Vijay Madisetti
ISBN:
Format: PDF, Docs
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In response to requests for instructional and training material from instructors, we prepared this laboratory training guide as a companion book to the Cloud Computing: A Hands-On Approach ("Cloud Book"). This book is designed to serve two purposes. First, it provides a tutorial for the laboratory training that can accompany traditional or online instruction using the Cloud Book. Second, it provides access to the complete source code used in the examples provided in the Cloud Book. The authors hope that this laboratory training guide will continue to prove useful to instructors and students using the Cloud Book.

Internet of Things A Hands On Approach

Author: Arshdeep Bahga
Publisher: VPT
ISBN: 0996025510
Format: PDF, Mobi
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Internet of Things (IoT) refers to physical and virtual objects that have unique identities and are connected to the internet to facilitate intelligent applications that make energy, logistics, industrial control, retail, agriculture and many other domains "smarter". Internet of Things is a new revolution of the Internet that is rapidly gathering momentum driven by the advancements in sensor networks, mobile devices, wireless communications, networking and cloud technologies. Experts forecast that by the year 2020 there will be a total of 50 billion devices/things connected to the internet. This book is written as a textbook on Internet of Things for educational programs at colleges and universities, and also for IoT vendors and service providers who may be interested in offering a broader perspective of Internet of Things to accompany their own customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. Like our companion book on Cloud Computing, we have tried to write a comprehensive book that transfers knowledge through an immersive "hands on" approach, where the reader is provided the necessary guidance and knowledge to develop working code for real-world IoT applications. Additional support is available at the book's website: www.internet-of-things-book.com Organization The book is organized into 3 main parts, comprising of a total of 11 chapters. Part I covers the building blocks of Internet of Things (IoTs) and their characteristics. A taxonomy of IoT systems is proposed comprising of various IoT levels with increasing levels of complexity. Domain specific Internet of Things and their real-world applications are described. A generic design methodology for IoT is proposed. An IoT system management approach using NETCONF-YANG is described. Part II introduces the reader to the programming aspects of Internet of Things with a view towards rapid prototyping of complex IoT applications. We chose Python as the primary programming language for this book, and an introduction to Python is also included within the text to bring readers to a common level of expertise. We describe packages, frameworks and cloud services including the WAMP-AutoBahn, Xively cloud and Amazon Web Services which can be used for developing IoT systems. We chose the Raspberry Pi device for the examples in this book. Reference architectures for different levels of IoT applications are examined in detail. Case studies with complete source code for various IoT domains including home automation, smart environment, smart cities, logistics, retail, smart energy, smart agriculture, industrial control and smart health, are described. Part III introduces the reader to advanced topics on IoT including IoT data analytics and Tools for IoT. Case studies on collecting and analyzing data generated by Internet of Things in the cloud are described.

Big Data Science Analytics

Author: Arshdeep Bahga
Publisher: Vpt
ISBN: 9780996025539
Format: PDF
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We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.

Cloud Computing

Author: Christian Baun
Publisher: Springer-Verlag
ISBN: 3642184367
Format: PDF, Kindle
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Als Internetdienst erlaubt Cloud Computing die Bereitstellung und Nutzung von IT-Infrastruktur, Plattformen und Anwendungen. Dabei wird stets die aktuell benötigte Menge an Ressourcen zur Verfügung gestellt und abgerechnet. In dem Buch vermitteln die Autoren einen Überblick über Cloud-Computing-Architektur, ihre Anwendungen und Entwicklung. Dieses erste deutschsprachige Buch zum Thema liefert u. a. eine standardisierende Erläuterung der oft unterschiedlich verwendeten Begriffe. Technische Vorkenntnisse sind für das Verständnis nicht erforderlich.

Cloud Migration

Author: Hö
Publisher: MITP-Verlags GmbH & Co. KG
ISBN: 382669225X
Format: PDF, Kindle
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Manager und Unternehmer, die uber die Okonomisierung ihrer IT nachdenken mussen, sowie Leser, die mehr uber die Cloud wissen, aber keine Spezialisten werden wollen, sollen in diesem Buch nutzliche Informationen uber die Moglichkeiten finden, die Cloud-Computing bieten kann. Technische, rechtliche, steuerliche, betriebswirtschaftliche, organisatorische und okologische Aspekte beim Einsatz von Cloud-Services werden umfassend dargestellt, dazu werden praxisrelevante Vorschlage zur Anbieterauswahl und Zertifizierung gemacht sowie konkrete Fallstudien zu Cloud-Projekten prasentiert. Sechs Mastereditoren und 51 Autoren aus elf Landern trugen durch produktive und effiziente Zusammenarbeit zur Entstehung dieses Buches bei. Beteiligt waren Juristen, Steuerberater, Techniker, Betriebswirte, Unternehmensberater und eine Reihe von weiteren Personen, die fur den mehrfachen Review und die Qualitatssicherung verantwortlich zeichneten.

Data Science f r Dummies

Author: Lillian Pierson
Publisher: John Wiley & Sons
ISBN: 352780675X
Format: PDF, ePub, Docs
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Daten, Daten, Daten ?, Sie haben schon Kenntnisse in Excel und Statistik, wissen aber noch nicht, wie all die Datensï¿1⁄2tze helfen sollen, bessere Entscheidungen zu treffen? Von Lillian Pierson bekommen Sie das dafï¿1⁄2r notwendige Handwerkszeug: Bauen Sie Ihre Kenntnisse in Statistik, Programmierung und Visualisierung aus. Nutzen Sie Python, R, SQL, Excel und KNIME. Zahlreiche Beispiele veranschaulichen die vorgestellten Methoden und Techniken. So kï¿1⁄2nnen Sie die Erkenntnisse dieses Buches auf Ihre Daten ï¿1⁄2bertragen und aus deren Analyse unmittelbare Schlï¿1⁄2sse und Konsequenzen ziehen.

CompTIA Cloud Study Guide

Author: Todd Montgomery
Publisher: John Wiley & Sons
ISBN: 1119243238
Format: PDF
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A hands-on approach to cloud computing for Exam CV0-001 CompTIA Cloud+ Study Guide covers 100% of all exam CV0-001objectives with in-depth explanations from expert Todd Montgomery. This comprehensive resource covers all aspects of cloud computing infrastructure and administration, with a practical focus on real-world skills. Each chapter includes a list of exam topics, helpful hands-on exercises, and illustrative examples that show how concepts are applied in different scenarios, to help you build a solid foundation of cloud computing skills. You also gain access to the Sybex interactive online learning environment and test bank, featuring electronic flashcards, glossary of key terms, and chapter tests and practice exams that help you test your knowledge and gauge the extent of your understanding. CompTIA's Cloud+ certification covers the implementation, maintenance, delivery, and security of cloud technologies and infrastructure. With thorough coverage, practical instruction, and expert insight, this book provides an ideal resource for Exam CV0-001 preparation. Master the fundamental concepts, terminology, and characteristics of cloud computing Implement cloud solutions, manage the infrastructure, and monitor performance Install, configure, and manage virtual machines and devices Get up to speed on hardware, testing, deployment, and more The Cloud+ certification identifies you as the professional these companies need to ensure safe, seamless, functional cloud services, and The CompTIA Cloud+ Study Guide Exam CV0-001 provides the tools you need to be confident on exam day.

Rechnerorganisation und Rechnerentwurf

Author: David Patterson
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110446065
Format: PDF, Docs
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Mit der deutschen Übersetzung zur fünfter Auflage des amerikanischen Klassikers Computer Organization and Design - The Hardware/Software Interface ist das Standardwerk zur Rechnerorganisation wieder auf dem neusten Stand - David A. Patterson und John L. Hennessy gewähren die gewohnten Einblicke in das Zusammenwirken von Hard- und Software, Leistungseinschätzungen und zahlreicher Rechnerkonzepte in einer Tiefe, die zusammen mit klarer Didaktik und einer eher lockeren Sprache den Erfolg dieses weltweit anerkannten Standardwerks begründen. Patterson und Hennessy achten darauf, nicht nur auf das "Wie" der dargestellten Konzepte, sondern auch auf ihr "Warum" einzugehen und zeigen damit Gründe für Veränderungen und neue Entwicklungen auf. Jedes der Kapitel steht für einen deutlich umrissenen Teilbereich der Rechnerorganisation und ist jeweils gleich aufgebaut: Eine Einleitung, gefolgt von immer tiefgreifenderen Grundkonzepten mit steigernder Komplexität. Darauf eine aktuelle Fallstudie, "Fallstricke und Fehlschlüsse", Zusammenfassung und Schlussbetrachtung, historische Perspektiven und Literaturhinweise sowie Aufgaben. In der neuen Auflage sind die Inhalte in den Kapiteln 1-5 an vielen Stellen punktuell verbessert und aktualisiert, mit der Vorstellung neuerer Prozessoren worden, und der Kapitel 6... from Client to Cloud wurde stark überarbeitetUmfangreiches Zusatzmaterial (Werkzeuge mit Tutorien etc.) stehtOnline zur Verfügung.

Statistik Workshop f r Programmierer

Author: Allen B. Downey
Publisher: O'Reilly Germany
ISBN: 3868993436
Format: PDF, ePub, Mobi
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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.