Scalable Big Data Architecture PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Scalable Big Data Architecture PDF full book. Access full book title Scalable Big Data Architecture by Bahaaldine Azarmi. Download full books in PDF and EPUB format.
Author: Bahaaldine Azarmi
Publisher: Apress
ISBN: 1484213262
Category : Computers
Languages : en
Pages : 147
Get Book Here
Book Description
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
Author: Bahaaldine Azarmi
Publisher: Apress
ISBN: 1484213262
Category : Computers
Languages : en
Pages : 147
Get Book Here
Book Description
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it’s often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
Author:
Publisher:
ISBN:
Category : Expert systems (Computer science)
Languages : en
Pages : 668
Get Book Here
Book Description
Author: Laure Berti-Équille
Publisher: Morgan & Claypool Publishers
ISBN: 1627057722
Category : Computers
Languages : en
Pages : 157
Get Book Here
Book Description
In the Web, a massive amount of user-generated contents are available through various channels (e.g., texts, tweets, Web tables, databases, multimedia-sharing platforms, etc.). Conflicting information, rumors, erroneous and fake contents can be easily spread across multiple sources, making it hard to distinguish between what is true and what is not. This monograph gives an overview of fundamental issues and recent contributions for ascertaining the veracity of data in the era of Big Data. The text is organized into six chapters, focusing on structured data extracted from texts. Chapter One introduces the problem of ascertaining the veracity of data in a multi-source and evolving context. Issues related to information extraction are presented in chapter Two. It is followed by practical techniques for evaluating data source reputation and authoritativeness in Chapter Three, including a review of the main models and Bayesian approaches of trust management. Current truth discovery computation algorithms are presented in details in Chapter Four. The theoretical foundations and various approaches for modeling diffusion phenomenon of misinformation spreading in networked systems is studied in Chapter Five. Finally, truth discovery computation from extracted data in a dynamic context of misinformation propagation raises interesting challenges that are explored in Chapter Six. Supplementary material including source codes, datasets, and slides are offered online. This text is intended for a seminar course at the graduate level. It is also to serve as a useful resource for researchers and practitioners who are interested in the study of fact-checking, truth discovery or rumor spreading.
Author: International Society for Photogrammetry and Remote Sensing. Congress
Publisher:
ISBN:
Category : Photogrammetry
Languages : en
Pages : 1000
Get Book Here
Book Description
Author: Gérard Dreyfus
Publisher: Springer Science & Business Media
ISBN: 3540288473
Category : Science
Languages : en
Pages : 509
Get Book Here
Book Description
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
Author:
Publisher:
ISBN:
Category : Computers
Languages : fr
Pages : 424
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Cartography
Languages : en
Pages : 406
Get Book Here
Book Description
Author: Ali Emrouznejad
Publisher: Springer
ISBN: 3319302655
Category : Technology & Engineering
Languages : en
Pages : 492
Get Book Here
Book Description
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Author: Jean-Marc Pivot
Publisher: Editions Quae
ISBN: 9782853624947
Category : Agriculture and state
Languages : fr
Pages : 380
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Cybernetics
Languages : en
Pages : 540
Get Book Here
Book Description
