Modern Big Data Architectures
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Modern Big Data Architectures
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Author : Dominik Ryzko
language : en
Publisher: John Wiley & Sons
Release Date : 2020-03-31
Modern Big Data Architectures written by Dominik Ryzko and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-31 with Computers categories.
Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.
Scalable Big Data Architecture
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Author : Bahaaldine Azarmi
language : en
Publisher: Apress
Release Date : 2015-12-31
Scalable Big Data Architecture written by Bahaaldine Azarmi and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-31 with Computers categories.
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-SQLto 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 tointegrate into that pattern.
Modern Big Data Architectures
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Author : Dominik Ryzko
language : en
Publisher: John Wiley & Sons
Release Date : 2020-04-09
Modern Big Data Architectures written by Dominik Ryzko and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-09 with Computers categories.
Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.
Modern Big Data Processing With Hadoop
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Author : V Naresh Kumar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Modern Big Data Processing With Hadoop written by V Naresh Kumar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Computers categories.
A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop Key Features -Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform -Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more -A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Book Description The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. What you will learn Build an efficient enterprise Big Data strategy centered around Apache Hadoop Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari Design effective streaming data pipelines and build your own enterprise search solutions Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset Plan, set up and administer your Hadoop cluster efficiently Who this book is for This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book.
Knowledge Graphs And Big Data Processing
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Author : Valentina Janev
language : en
Publisher: Springer Nature
Release Date : 2020-07-15
Knowledge Graphs And Big Data Processing written by Valentina Janev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-15 with Computers categories.
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
Real Time Big Data Analytics Emerging Architecture
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Author : Mike Barlow
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-06-24
Real Time Big Data Analytics Emerging Architecture written by Mike Barlow and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-24 with Computers categories.
Five or six years ago, analysts working with big datasets made queries and got the results back overnight. The data world was revolutionized a few years ago when Hadoop and other tools made it possible to getthe results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.
Big Data Application Architecture Q A
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Author : Nitin Sawant
language : en
Publisher: Apress
Release Date : 2014-01-24
Big Data Application Architecture Q A written by Nitin Sawant and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-24 with Computers categories.
Big Data Application Architecture Pattern Recipes provides an insight into heterogeneous infrastructures, databases, and visualization and analytics tools used for realizing the architectures of big data solutions. Its problem-solution approach helps in selecting the right architecture to solve the problem at hand. In the process of reading through these problems, you will learn harness the power of new big data opportunities which various enterprises use to attain real-time profits. Big Data Application Architecture Pattern Recipes answers one of the most critical questions of this time 'how do you select the best end-to-end architecture to solve your big data problem?'. The book deals with various mission critical problems encountered by solution architects, consultants, and software architects while dealing with the myriad options available for implementing a typical solution, trying to extract insight from huge volumes of data in real–time and across multiple relational and non-relational data types for clients from industries like retail, telecommunication, banking, and insurance. The patterns in this book provide the strong architectural foundation required to launch your next big data application. The architectures for realizing these opportunities are based on relatively less expensive and heterogeneous infrastructures compared to the traditional monolithic and hugely expensive options that exist currently. This book describes and evaluates the benefits of heterogeneity which brings with it multiple options of solving the same problem, evaluation of trade-offs and validation of 'fitness-for-purpose' of the solution.
Architecting Modern Data Platforms
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Author : Jan Kunigk
language : en
Publisher: O'Reilly Media
Release Date : 2018
Architecting Modern Data Platforms written by Jan Kunigk and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Apache Hadoop categories.
There's a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you'll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You'll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability
Next Generation Big Data
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Author : Butch Quinto
language : en
Publisher: Apress
Release Date : 2018-06-12
Next Generation Big Data written by Butch Quinto and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-12 with Computers categories.
Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics
A Modern Enterprise Architecture Approach
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Author : Dr Mehmet Yildiz
language : en
Publisher: Steps Publishing Australia
Release Date : 2019-10-07
A Modern Enterprise Architecture Approach written by Dr Mehmet Yildiz and has been published by Steps Publishing Australia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-07 with Computers categories.
The revised version of this book to provide essential guidance, compelling ideas, and unique ways to Enterprise Architects so that they can successfully perform complex enterprise modernisation initiatives transforming from chaos to coherence. This is not an ordinary theory book describing Enterprise Architecture in detail. There are myriad of books on the market and in libraries discussing details of enterprise architecture. My aim here is to highlight success factors and reflect lessons learnt from the field within enterprise modernisation and transformation context. As a practising Senior Enterprise Architect, myself, I read hundreds of those books and articles to learn different views. They have been valuable to me to establish my foundations in the earlier phase of my profession. However, what is missing now is a concise guidance book showing Enterprise Architects the novel approaches, insights from the real-life experience and experimentations, and pointing out the differentiating technologies for enterprise modernisation. If only there were such a guide when I started engaging in modernisation and transformation programs. The biggest lesson learned is the business outcome of the enterprise modernisation. What genuinely matters for business is the return on investment of the enterprise architecture and its monetising capabilities. The rest is the theory because nowadays sponsoring executives, due to economic climate, have no interest, attention, or tolerance for non-profitable ventures. I am sorry for disappointing some idealistic Enterprise Architects, but with due respect, it is the reality, and we cannot change it. This book deals with reality rather than theoretical perfection. Anyone against this view on this climate must be coming from another planet. In this concise, uncluttered and easy-to-read book, I attempt to show the significant pain points and valuable considerations for enterprise modernisation using a structured approach and a simple narration especially considering my audience from non-English speaking backgrounds. The architectural rigour is still essential. We cannot compromise the rigour aiming to the quality of products and services as a target outcome. However, there must be a delicate balance among architectural rigour, business value, and speed to the market. I applied this pragmatic approach to multiple substantial transformation initiatives and complex modernisations programs. The key point is using an incrementally progressing iterative approach to every aspect of modernisation initiatives, including people, processes, tools, and technologies as a whole. Starting with a high-level view of enterprise architecture to set the context, I provided a dozen of distinct chapters to point out and elaborate on the factors which can make a real difference in dealing with complexity and producing excellent modernisation initiatives. As eminent leaders, Enterprise Architects are the critical talents who can undertake this massive mission using their people and technology skills, in addition to many critical attributes such as calm and composed approach. Let's keep in mind that as Enterprise Architects, we are architects, not firefighters! I have full confidence that this book can provide valuable insights and some 'aha' moments for talented architects like yourself to tackle this enormous mission of turning chaos to coherence.