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Learning Apache Cassandra Manage Fault Tolerant And Scalable Real Time Data

Author: Ellis C. Wallace
Publisher: CreateSpace
ISBN: 9781516955237
Size: 57.11 MB
Format: PDF, Mobi
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This updated and expanded second edition of the Learning Apache Cassandra - Manage Fault Tolerant and Scalable Real-Time Data provides a user-friendly introduction to the subject Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject . We hope you find this book useful in shaping your future career & Business.

Lecture Notes In Real Time Intelligent Systems

Author: Jolanta Mizera-Pietraszko
Publisher: Springer
ISBN: 3319913379
Size: 80.44 MB
Format: PDF, ePub, Mobi
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The second volume of the book series highlights works presented at the 2nd International Conference on Real Time Intelligent Systems, held in Casablanca on October 18-20, 2017​. The book offers a comprehensive, practical review of the state-of-the-art in designing and implementing real-time intelligent computing for the areas within the conference’s scope such as robotics, intelligent alert systems, IoT, remote access control, multi-agent systems, networking, mobile smart systems, crowdsourcing, broadband systems, cloud computing, streaming data and many other applications. Research in real-time computing supports decision making in dynamic environments. Some examples include ABS, FBW flight control, automatic air-conditioning, etc. Intelligent computing relies heavily on artificial intelligence (AI) to make computers act for humans. The authors are confident that the solutions discussed in this book will provide a unique source of information and inspiration for researchers working in AI, distributed coding algorithms or smart services and platforms, and for IT professionals, who can integrate the proposed methods into their practice.

Deep Learning Convergence To Big Data Analytics

Author: Murad Khan
Publisher: Springer
ISBN: 9811334595
Size: 17.40 MB
Format: PDF, Docs
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This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Data Analytics

Author: Mohiuddin Ahmed
Publisher: CRC Press
ISBN: 0429820909
Size: 64.48 MB
Format: PDF
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Large data sets arriving at every increasing speeds require a new set of efficient data analysis techniques. Data analytics are becoming an essential component for every organization and technologies such as health care, financial trading, Internet of Things, Smart Cities or Cyber Physical Systems. However, these diverse application domains give rise to new research challenges. In this context, the book provides a broad picture on the concepts, techniques, applications, and open research directions in this area. In addition, it serves as a single source of reference for acquiring the knowledge on emerging Big Data Analytics technologies.

Learning Real Time Processing With Spark Streaming

Author: Sumit Gupta
Publisher: Packt Publishing Ltd
ISBN: 1783987677
Size: 72.82 MB
Format: PDF, ePub, Mobi
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Building scalable and fault-tolerant streaming applications made easy with Spark streaming About This Book Process live data streams more efficiently with better fault recovery using Spark Streaming Implement and deploy real-time log file analysis Learn about integration with Advance Spark Libraries – GraphX, Spark SQL, and MLib. Who This Book Is For This book is intended for big data developers with basic knowledge of Scala but no knowledge of Spark. It will help you grasp the basics of developing real-time applications with Spark and understand efficient programming of core elements and applications. What You Will Learn Install and configure Spark and Spark Streaming to execute applications Explore the architecture and components of Spark and Spark Streaming to use it as a base for other libraries Process distributed log files in real-time to load data from distributed sources Apply transformations on streaming data to use its functions Integrate Apache Spark with the various advance libraries like MLib and GraphX Apply production deployment scenarios to deploy your application In Detail Using practical examples with easy-to-follow steps, this book will teach you how to build real-time applications with Spark Streaming. Starting with installing and setting the required environment, you will write and execute your first program for Spark Streaming. This will be followed by exploring the architecture and components of Spark Streaming along with an overview of libraries/functions exposed by Spark. Next you will be taught about various client APIs for coding in Spark by using the use-case of distributed log file processing. You will then apply various functions to transform and enrich streaming data. Next you will learn how to cache and persist datasets. Moving on you will integrate Apache Spark with various other libraries/components of Spark like Mlib, GraphX, and Spark SQL. Finally, you will learn about deploying your application and cover the different scenarios ranging from standalone mode to distributed mode using Mesos, Yarn, and private data centers or on cloud infrastructure. Style and approach A Step-by-Step approach to learn Spark Streaming in a structured manner, with detailed explanation of basic and advance features in an easy-to-follow Style. Each topic is explained sequentially and supported with real world examples and executable code snippets that appeal to the needs of readers with the wide range of experiences.

Fast Data Processing Systems With Smack Stack

Author: Raul Estrada
Publisher: Packt Publishing Ltd
ISBN: 1786468069
Size: 53.72 MB
Format: PDF, ePub
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Combine the incredible powers of Spark, Mesos, Akka, Cassandra, and Kafka to build data processing platforms that can take on even the hardest of your data troubles! About This Book This highly practical guide shows you how to use the best of the big data technologies to solve your response-critical problems Learn the art of making cheap-yet-effective big data architecture without using complex Greek-letter architectures Use this easy-to-follow guide to build fast data processing systems for your organization Who This Book Is For If you are a developer, data architect, or a data scientist looking for information on how to integrate the Big Data stack architecture and how to choose the correct technology in every layer, this book is what you are looking for. What You Will Learn Design and implement a fast data Pipeline architecture Think and solve programming challenges in a functional way with Scala Learn to use Akka, the actors model implementation for the JVM Make on memory processing and data analysis with Spark to solve modern business demands Build a powerful and effective cluster infrastructure with Mesos and Docker Manage and consume unstructured and No-SQL data sources with Cassandra Consume and produce messages in a massive way with Kafka In Detail SMACK is an open source full stack for big data architecture. It is a combination of Spark, Mesos, Akka, Cassandra, and Kafka. This stack is the newest technique developers have begun to use to tackle critical real-time analytics for big data. This highly practical guide will teach you how to integrate these technologies to create a highly efficient data analysis system for fast data processing. We'll start off with an introduction to SMACK and show you when to use it. First you'll get to grips with functional thinking and problem solving using Scala. Next you'll come to understand the Akka architecture. Then you'll get to know how to improve the data structure architecture and optimize resources using Apache Spark. Moving forward, you'll learn how to perform linear scalability in databases with Apache Cassandra. You'll grasp the high throughput distributed messaging systems using Apache Kafka. We'll show you how to build a cheap but effective cluster infrastructure with Apache Mesos. Finally, you will deep dive into the different aspect of SMACK using a few case studies. By the end of the book, you will be able to integrate all the components of the SMACK stack and use them together to achieve highly effective and fast data processing. Style and approach With the help of various industry examples, you will learn about the full stack of big data architecture, taking the important aspects in every technology. You will learn how to integrate the technologies to build effective systems rather than getting incomplete information on single technologies. You will learn how various open source technologies can be used to build cheap and fast data processing systems with the help of various industry examples

Scala And Spark For Big Data Analytics

Author: Md. Rezaul Karim
Publisher: Packt Publishing Ltd
ISBN: 1783550503
Size: 57.25 MB
Format: PDF, Kindle
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Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye! About This Book Learn Scala's sophisticated type system that combines Functional Programming and object-oriented concepts Work on a wide array of applications, from simple batch jobs to stream processing and machine learning Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark Who This Book Is For Anyone who wishes to learn how to perform data analysis by harnessing the power of Spark will find this book extremely useful. No knowledge of Spark or Scala is assumed, although prior programming experience (especially with other JVM languages) will be useful to pick up concepts quicker. What You Will Learn Understand object-oriented & functional programming concepts of Scala In-depth understanding of Scala collection APIs Work with RDD and DataFrame to learn Spark's core abstractions Analysing structured and unstructured data using SparkSQL and GraphX Scalable and fault-tolerant streaming application development using Spark structured streaming Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML Build clustering models to cluster a vast amount of data Understand tuning, debugging, and monitoring Spark applications Deploy Spark applications on real clusters in Standalone, Mesos, and YARN In Detail Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big. Style and approach Filled with practical examples and use cases, this book will hot only help you get up and running with Spark, but will also take you farther down the road to becoming a data scientist.

Entwicklung Eines Skalierbaren Und Verteilten Datenbanksystems

Author: Jan Kristof Nidzwetzki
Publisher: Springer-Verlag
ISBN: 365812444X
Size: 13.34 MB
Format: PDF, ePub, Docs
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Jan Kristof Nidzwetzki hat in seiner Masterarbeit ein erweiterbares Datenbanksystem mit einem hochverfügbaren Key-Value-Store gekoppelt und untersucht, wie sich die Vorteile beider Systeme kombinieren lassen. Im Gegensatz zu Datenbanksystemen skalieren Key-Value-Stores sehr gut, bieten jedoch nur sehr einfache Operationen für die Abfrageauswertung an. Durch die Kopplung ergibt sich ein skalierbares, ausfallsicheres System, das in der Lage ist, beliebige Updateraten zu unterstützen und auf den gespeicherten Daten komplexe Abfragen auszuführen.

Big Data Fast Data

Author: Michael Lex
Publisher:
ISBN: 3868027394
Size: 32.15 MB
Format: PDF, Kindle
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Die Big-Data-Welt verändert sich. Mit diesem shortcut erfahren Sie, was hinter den Begriffen Fast Data und SMACK steckt, wie Daten mittels Kafka und Akka ins System kommen und auf welche Art und Weise eine Datenanalyse mit Spark und Apache Zeppelin funktioniert. Im abschließenden Kapitel erläutern die Autoren, wie Daten unter Verwendung von Spark und Cassandra gespeichert, verarbeitet, aktualisiert und mit weiteren Informationen zusammengebracht werden können.