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| big data technology stack: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| big data technology stack: Real-Time Big Data Analytics: Emerging Architecture Mike Barlow, 2013-06-24 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 technology stack: Big Data SMACK Raul Estrada, Isaac Ruiz, 2016-09-29 Learn how to integrate full-stack open source big data architecture and to choose the correct technology—Scala/Spark, Mesos, Akka, Cassandra, and Kafka—in every layer. Big data architecture is becoming a requirement for many different enterprises. So far, however, the focus has largely been on collecting, aggregating, and crunching large data sets in a timely manner. In many cases now, organizations need more than one paradigm to perform efficient analyses. Big Data SMACK explains each of the full-stack technologies and, more importantly, how to best integrate them. It provides detailed coverage of the practical benefits of these technologies and incorporates real-world examples in every situation. This book focuses on the problems and scenarios solved by the architecture, as well as the solutions provided by every technology. It covers the six main concepts of big data architecture and how integrate, replace, and reinforce every layer: The language: Scala The engine: Spark (SQL, MLib, Streaming, GraphX) The container: Mesos, Docker The view: Akka The storage: Cassandra The message broker: Kafka What You Will Learn: Make big data architecture without using complex Greek letter architectures Build a cheap but effective cluster infrastructure Make queries, reports, and graphs that business demands Manage and exploit unstructured and No-SQL data sources Use tools to monitor the performance of your architecture Integrate all technologies and decide which ones replace and which ones reinforce Who This Book Is For: Developers, data architects, and data scientists looking to integrate the most successful big data open stack architecture and to choose the correct technology in every layer |
| big data technology stack: Next-Generation Big Data Butch Quinto, 2018-06-12 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 |
| big data technology stack: Big Data Strategies for Agile Business Bhuvan Unhelkar, 2017-09-13 Agile is a set of values, principles, techniques, and frameworks for the adaptable, incremental, and efficient delivery of work. Big Data is a rapidly growing field that encompasses crucial aspects of data such as its volume, velocity, variety, and veracity. This book outlines a strategic approach to Big Data that will render a business Agile. It discusses the important competencies required to streamline and focus on the analytics and presents a roadmap for implementing such analytics in business. |
| big data technology stack: The Enterprise Big Data Framework Jan-Willem Middelburg, 2023-11-03 Businesses who can make sense of the huge influx and complexity of data will be the big winners in the information economy. This comprehensive guide covers all the aspects of transforming enterprise data into value, from the initial set-up of a big data strategy, towards algorithms, architecture and data governance processes. Using a vendor-independent approach, The Enterprise Big Data Framework offers practical advice on how to develop data-driven decision making, detailed data analysis and data engineering techniques. With a focus on business implementation, The Enterprise Big Data Framework includes sections on analysis, engineering, algorithm design and big data architecture, and covers topics such as data preparation and presentation, data modelling, data science, programming languages and machine learning algorithms. Endorsed by leading accreditation and examination institute AMPG International, this book is required reading for the Enterprise Big Data Certifications, which aim to develop excellence in big data practices across the globe. Online resources include sample data for practice purposes. |
| big data technology stack: Big Data Processing Using Spark in Cloud Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar, 2018-06-16 The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data. The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments. |
| big data technology stack: Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities Segall, Richard S., Niu, Gao, 2020-02-21 With the development of computing technologies in today’s modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines. |
| big data technology stack: Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing Velayutham, Sathiyamoorthi, 2021-01-29 In today’s market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues. |
| big data technology stack: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. |
| big data technology stack: Big Data Analytics Venkat Ankam, 2016-09-28 A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop clusters About This Book This book is based on the latest 2.0 version of Apache Spark and 2.7 version of Hadoop integrated with most commonly used tools. Learn all Spark stack components including latest topics such as DataFrames, DataSets, GraphFrames, Structured Streaming, DataFrame based ML Pipelines and SparkR. Integrations with frameworks such as HDFS, YARN and tools such as Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector, GraphFrames, H2O and Hivemall. Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. It is assumed that you have basic programming background in Scala, Python, SQL, or R programming with basic Linux experience. Working experience within big data environments is not mandatory. What You Will Learn Find out and implement the tools and techniques of big data analytics using Spark on Hadoop clusters with wide variety of tools used with Spark and Hadoop Understand all the Hadoop and Spark ecosystem components Get to know all the Spark components: Spark Core, Spark SQL, DataFrames, DataSets, Conventional and Structured Streaming, MLLib, ML Pipelines and Graphx See batch and real-time data analytics using Spark Core, Spark SQL, and Conventional and Structured Streaming Get to grips with data science and machine learning using MLLib, ML Pipelines, H2O, Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data. Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples. Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science |
| big data technology stack: The Human Element of Big Data Geetam S. Tomar, Narendra S. Chaudhari, Robin Singh Bhadoria, Ganesh Chandra Deka, 2016-10-26 The proposed book talks about the participation of human in Big Data.How human as a component of system can help in making the decision process easier and vibrant.It studies the basic build structure for big data and also includes advanced research topics.In the field of Biological sciences, it comprises genomic and proteomic data also. The book swaps traditional data management techniques with more robust and vibrant methodologies that focus on current requirement and demand through human computer interfacing in order to cope up with present business demand. Overall, the book is divided in to five parts where each part contains 4-5 chapters on versatile domain with human side of Big Data. |
| big data technology stack: Emerging Perspectives in Big Data Warehousing Taniar, David, Rahayu, Wenny, 2019-06-28 The concept of a big data warehouse appeared in order to store moving data objects and temporal data information. Moving objects are geometries that change their position and shape continuously over time. In order to support spatio-temporal data, a data model and associated query language is needed for supporting moving objects. Emerging Perspectives in Big Data Warehousing is an essential research publication that explores current innovative activities focusing on the integration between data warehousing and data mining with an emphasis on the applicability to real-world problems. Featuring a wide range of topics such as index structures, ontology, and user behavior, this book is ideally designed for IT consultants, researchers, professionals, computer scientists, academicians, and managers. |
| big data technology stack: Knowledge Graphs and Big Data Processing Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger, 2020-07-15 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. |
| big data technology stack: Handbook of Research on Big Data Storage and Visualization Techniques Segall, Richard S., Cook, Jeffrey S., 2018-01-05 The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject. |
| big data technology stack: Big Data Analytics for Smart and Connected Cities Dey, Nilanjan, Tamane, Sharvari, 2018-09-07 To continue providing people with safe, comfortable, and affordable places to live, cities must incorporate techniques and technologies to bring them into the future. The integration of big data and interconnected technology, along with the increasing population, will lead to the necessary creation of smart cities. Big Data Analytics for Smart and Connected Cities is a pivotal reference source that provides vital research on the application of the integration of interconnected technologies and big data analytics into the creation of smart cities. While highlighting topics such as energy conservation, public transit planning, and performance measurement, this publication explores technology integration in urban environments as well as the methods of planning cities to implement these new technologies. This book is ideally designed for engineers, professionals, researchers, and technology developers seeking current research on technology implementation in urban settings. |
| big data technology stack: Big Data Infrastructure Technologies for Data Analytics Yuri Demchenko, |
| big data technology stack: Big Data Fei Hu, 2016-04-27 Although there are already some books published on Big Data, most of them only cover basic concepts and society impacts and ignore the internal implementation details-making them unsuitable to R&D people. To fill such a need, Big Data: Storage, Sharing, and Security examines Big Data management from an R&D perspective. It covers the 3S desi |
| big data technology stack: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| big data technology stack: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| big data technology stack: Hadoop: The Definitive Guide Tom White, 2012-05-10 Ready to unlock the power of your data? With this comprehensive guide, you’ll learn how to build and maintain reliable, scalable, distributed systems with Apache Hadoop. This book is ideal for programmers looking to analyze datasets of any size, and for administrators who want to set up and run Hadoop clusters. You’ll find illuminating case studies that demonstrate how Hadoop is used to solve specific problems. This third edition covers recent changes to Hadoop, including material on the new MapReduce API, as well as MapReduce 2 and its more flexible execution model (YARN). Store large datasets with the Hadoop Distributed File System (HDFS) Run distributed computations with MapReduce Use Hadoop’s data and I/O building blocks for compression, data integrity, serialization (including Avro), and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster—or run Hadoop in the cloud Load data from relational databases into HDFS, using Sqoop Perform large-scale data processing with the Pig query language Analyze datasets with Hive, Hadoop’s data warehousing system Take advantage of HBase for structured and semi-structured data, and ZooKeeper for building distributed systems |
| big data technology stack: Effective Big Data Management and Opportunities for Implementation Singh, Manoj Kumar, G., Dileep Kumar, 2016-06-20 “Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data. |
| big data technology stack: Big Data Analytics for Sustainable Computing Haldorai, Anandakumar, Ramu, Arulmurugan, 2019-09-20 Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software. |
| big data technology stack: Decision Management: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2017-01-30 The implementation of effective decision making protocols is crucial in any organizational environment in modern society. Emerging advancements in technology and analytics have optimized uses and applications of decision making systems. Decision Management: Concepts, Methodologies, Tools, and Applications is a compendium of the latest academic material on the control, support, usage, and strategies for implementing efficient decision making systems across a variety of industries and fields. Featuring comprehensive coverage on numerous perspectives, such as data visualization, pattern analysis, and predictive analytics, this multi-volume book is an essential reference source for researchers, academics, professionals, managers, students, and practitioners interested in the maintenance and optimization of decision management processes. |
| big data technology stack: Machine Learning for Decision Makers Patanjali Kashyap, 2018-01-04 Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them. |
| big data technology stack: Cognitive Computing and Big Data Analytics Judith S. Hurwitz, Marcia Kaufman, Adrian Bowles, 2015-02-12 A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data. This book helps technologists understand cognitive computing's underlying technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches based on accumulated evidence, rather than reprogramming. Detailed case examples from the financial, healthcare, and manufacturing walk readers step-by-step through the design and testing of cognitive systems, and expert perspectives from organizations such as Cleveland Clinic, Memorial Sloan-Kettering, as well as commercial vendors that are creating solutions. These organizations provide insight into the real-world implementation of cognitive computing systems. The IBM Watson cognitive computing platform is described in a detailed chapter because of its significance in helping to define this emerging market. In addition, the book includes implementations of emerging projects from Qualcomm, Hitachi, Google and Amazon. Today's cognitive computing solutions build on established concepts from artificial intelligence, natural language processing, ontologies, and leverage advances in big data management and analytics. They foreshadow an intelligent infrastructure that enables a new generation of customer and context-aware smart applications in all industries. Cognitive Computing is a comprehensive guide to the subject, providing both the theoretical and practical guidance technologists need. Discover how cognitive computing evolved from promise to reality Learn the elements that make up a cognitive computing system Understand the groundbreaking hardware and software technologies behind cognitive computing Learn to evaluate your own application portfolio to find the best candidates for pilot projects Leverage cognitive computing capabilities to transform the organization Cognitive systems are rightly being hailed as the new era of computing. Learn how these technologies enable emerging firms to compete with entrenched giants, and forward-thinking established firms to disrupt their industries. Professionals who currently work with big data and analytics will see how cognitive computing builds on their foundation, and creates new opportunities. Cognitive Computing provides complete guidance to this new level of human-machine interaction. |
| big data technology stack: Software Architecture Henry Muccini, Paris Avgeriou, Barbora Buhnova, Javier Camara, Mauro Caporuscio, Mirco Franzago, Anne Koziolek, Patrizia Scandurra, Catia Trubiani, Danny Weyns, Uwe Zdun, 2020-09-10 This book constitutes the refereed proceedings of the tracks and workshops which complemented the 14th European Conference on Software Architecture, ECSA 2020, held in L'Aquila, Italy*, in September 2020. The 30 full papers and 9 short papers presented in this volume were carefully reviewed and selected from 72 submissions. Papers presented were accepted into the following tracks and workshops: ECSA 2020 Doctoral Symposium track; ECSA 2020 Tool Demos track; ECSA 2020 Gender Diversity in Software Architecture &Software Engineering track; CASA - 3rd International Workshop on Context-aware, Autonomous and Smart Architecture; CSE/QUDOS - Joint Workshop on Continuous Software Engineering and Quality-Aware DevOps; DETECT - 3rd International Workshop on Modeling, Verication and Testing of Dependable Critical Systems; FAACS-MDE4SA - Joint Workshop on Formal Approaches for Advanced Computing Systems and Model-Driven Engineering for Software Architecture; IoT-ASAP - 4th International Workshop on Engineering IoT Systems: Architectures, Services, Applications, and Platforms; SASI4 - 2nd Workshop on Systems, Architectures, and Solutions for Industry 4.0; WASA - 6th International Workshop on Automotive System/Software Architecture. *The conference was held virtually due to the COVID-19 pandemic. |
| big data technology stack: Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) Guiyun Guan, Bo Qu, Ding Zhou, 2023-02-11 This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. Such endeavors are making new contributions to the globalization and creating a community of shared future. The 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) was held on August 19 – 21, 2022, in Hulunbuir, China. With the support of experts and professors, the ICBDSS 2022 conference successfully held its first conference last year. In order to allow more scholars to have the opportunity to participate in the conference to share and exchange experience. This conference mainly focused on big data, social science and other research fields to discuss. At present, my country has entered the era of big data cloud migration, that is, the era of big data, the Internet of things, cloud computing and mobile Internet. The market demand for big data talents is also increasing day by day. The purpose of the conference is to provide a way for experts, scholars, engineering technicians, and technical R&D personnel engaged in big data and social science research to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote the academic achievement industry Platform for chemical cooperation. The conference sincerely invites experts, scholars from domestic and foreign universities, scientific research institutions, business people and other relevant personnel to participate in the conference. |
| big data technology stack: Complete Guide to Open Source Big Data Stack Michael Frampton, 2018-01-18 See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource management, processing, queuing, frameworks, data visualization, and more. What You’ll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for anyone interested in Hadoop or big data, and those experiencing problems with data size. |
| big data technology stack: BDEDM 2023 Misra Anuranjan, Ke Yan, Wang Yan, 2023-06-13 Proceedings of the 2nd International Conference on Big Data Economy and Digital Management (BDEDM 2023) supported by University Malaysia Sabah, Malaysia, held on 6th–8th January 2023 in Changsha, China (virtual conference). The immediate purpose of this Conference was to bring together experienced as well as young scientists who are interested in working actively on various aspects of Big Data Economy and Digital Management. The keynote speeches addressed major theoretical issues, current and forthcoming observational data as well as upcoming ideas in both theoretical and observational sectors. Keeping in mind the “academic exchange first” approach, the lectures were arranged in such a way that the young researchers had ample scope to interact with the stalwarts who are internationally leading experts in their respective fields of research. The major topics covered in the Conference are: Big Data in Enterprise Performance Management, Enterprise Management Modernization, Intelligent Management System, Performance Evaluation and Modeling Applications, Enterprise Technology Innovation, etc. |
| big data technology stack: AI-DRIVEN DATA ENGINEERING TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHT Eswar Prasad Galla, Chandrababu Kuraku, Hemanth Kumar Gollangi, Janardhana Rao Sunkara, Chandrakanth Rao Madhavaram, ..... |
| big data technology stack: Cloud Computing and Big Data in IoT Mr.Aashish Gadgil, Dr.Anjana Sangwan, Prof.Sulakshana Sagar Malwade, Prof.Megha Ashok Dhotay, 2024-04-02 Mr.Aashish Gadgil, Assistant Professor, Department of Electronics and Communication Engineering, KLS Gogte Institute of Technology, Belagavi, Karnataka, India. Dr.Anjana Sangwan, Associate Professor, Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, India. Prof.Sulakshana Sagar Malwade, Professor, Department of Polytechnic, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India. Prof.Megha Ashok Dhotay, Professor, Department of Polytechnic, Dr.Vishwanath Karad MIT World Peace University, Kothrud, Pune, Maharashtra, India. |
| big data technology stack: Managing and Processing Big Data in Cloud Computing Kannan, Rajkumar, 2016-01-07 Big data has presented a number of opportunities across industries. With these opportunities come a number of challenges associated with handling, analyzing, and storing large data sets. One solution to this challenge is cloud computing, which supports a massive storage and computation facility in order to accommodate big data processing. Managing and Processing Big Data in Cloud Computing explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution. Emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning, this publication meets the research needs of data analysts, IT professionals, researchers, graduate students, and educators in the areas of data science, computer programming, and IT development. |
| big data technology stack: Practical Data Science Andreas François Vermeulen, 2018-02-21 Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results Who This Book Is For Data scientists and data engineers who are required to convert data from a data lake into actionable knowledge for their business, and students who aspire to be data scientists and data engineers |
| big data technology stack: Fintech Pranay Gupta, T. Mandy Tham, 2018-12-03 This extraordinary book, written by leading players in a burgeoning technology revolution, is about the merger of finance and technology (fintech), and covers its various aspects and how they impact each discipline within the financial services industry. It is an honest and direct analysis of where each segment of financial services will stand. Fintech: The New DNA of Financial Services provides an in-depth introduction to understanding the various areas of fintech and terminology such as AI, big data, robo-advisory, blockchain, cryptocurrency, InsurTech, cloud computing, crowdfunding and many more. Contributions from fintech innovators discuss banking, insurance and investment management applications, as well as the legal and human resource implications of fintech in the future. |
| big data technology stack: Emerging Information Security and Applications Jiageng Chen, Debiao He, Rongxing Lu, 2023-01-03 This volume constitutes selected papers presented at the Third International Symposium on Emerging Information Security and Applications, EISA 2022, held in Wuhan, China, in October 2022. Due to COVID-19, EISA 2022 was held fully online. The 13 full papers presented in this volume were thoroughly reviewed and selected from the 35 submissions. They present a discussion on the emerging techniques, theories and applications to enhance information and application security in practice. |
| big data technology stack: Big Data Analytics with Java Rajat Mehta, 2017-07-31 Learn the basics of analytics on big data using Java, machine learning and other big data tools About This Book Acquire real-world set of tools for building enterprise level data science applications Surpasses the barrier of other languages in data science and learn create useful object-oriented codes Extensive use of Java compliant big data tools like apache spark, Hadoop, etc. Who This Book Is For This book is for Java developers who are looking to perform data analysis in production environment. Those who wish to implement data analysis in their Big data applications will find this book helpful. What You Will Learn Start from simple analytic tasks on big data Get into more complex tasks with predictive analytics on big data using machine learning Learn real time analytic tasks Understand the concepts with examples and case studies Prepare and refine data for analysis Create charts in order to understand the data See various real-world datasets In Detail This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naive Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world. Style and approach The approach of book is to deliver practical learning modules in manageable content. Each chapter is a self-contained unit of a concept in big data analytics. Book will step by step builds the competency in the area of big data analytics. Examples using real world case studies to give ideas of real applications and how to use the techniques mentioned. The examples and case studies will be shown using both theory and code. |
| big data technology stack: Engaged Learning and Innovative Teaching in Higher Education Will W. K. Ma, |
| big data technology stack: Big Data and Democracy Kevin Macnish, 2020-06-18 What's wrong with targeted advertising in political campaigns? Should we be worried about echo chambers? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges. It brings together original research on the philosophy of big data and democracy from leading international authors, with recent examples - including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks. And it asks whether an ethical compass is available or even feasible in an ever more digitised and monitored world. |
| big data technology stack: Architecting Modern Data Platforms Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George, 2018-12-05 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 |
THe HaDooP STaCk: New ParaDIGm for BIG DaTa …
In this article, we first present the new paradigm (in particular, the Hadoop stack) that is required for big data storage and processing. After that, we describe how to optimize the Hadoop …
The Data Science Technology Stack - NITRD
•Examples from the largest scale commercial big data systems. •My personal top five recommendations for critical technology investments for large data systems
Big Data, Hadoop, Map-Reduce - Middle East Technical …
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed …
Big Data Technology Stack [PDF] - www2.x-plane.com
big data technology stack: Big Data SMACK Raul Estrada, Isaac Ruiz, 2016-09-29 Learn how to integrate full-stack open source big data architecture and to choose the correct …
Big Data Technology Stack - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Big Data Architecture Patterns - BigRio
there are complex data architecture challenges both with designing the new “Big Data” stack as well as with integrating it with existing transactional and warehousing technologies. This paper …
Big Data - content.e-bookshelf.de
1 Introduction to the World of Big Data 1 1.1 Understanding Big Data 1 1.2 Evolution of Big Data 2 1.3 Failure of Traditional Database in Handling Big Data 3 1.4 3Vs of Big Data 4 1.5 Sources of …
Choosing a big data technology stack for digital marketing
Use this to develop a set of key decision vectors for choosing an appropriate digital technology stack for your data. Lay out a series of the most common digital marketing data use cases and …
International Journal of Engineering and Advanced …
This paper assesses the complete education on big data and various characterizes of big data like volume, velocity and variety. Survey about Big Data technologies along with their architecture …
Big Data Technology Stack (book) - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
InsideBIGDATA Guide to Big Data for Finance - ICDST
effectively exploit the big data technology stack, advanced statistical modeling, and predictive analytics in support of real-time decision making across business channels and operations will …
Big Data Technology Stack (PDF) - bubetech.com
Big Data Processing Using Spark in Cloud Mamta Mittal,Valentina E. Balas,Lalit Mohan Goyal,Raghvendra Kumar,2018-06-16 The book describes the emergence of big data …
Big Data Technology Stack - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Impact of the big data technology stack in digital marketing …
influence the performances itself identified as handling huge volumes of data, range of data modeling, data integration concession, supporting algorithmic queries, real-time support, …
Intro to Big Data - گروه آموزشی و پژوهشی سیب
Big Data Processing Frameworks Comparison •There are plenty of options for processing within a big data system. For batch-only workloads that are not time-sensitive, Hadoop is a good …
INtroduCtIoN to BIg data: INfrastruCture aNd NetworkINg …
Aug 27, 2012 · Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five …
MONITORING AND TROUBLESHOOTING BIG DATA …
As Big Data applications become increasingly integral to modern business operations, ensuring their seamless performance is critical. Monitoring and troubleshooting these applications pose …
Big Data Technology Stack (Download Only) - archive.ncarb.org
data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of …
Big Data Technology Stack Copy - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Big Data Technology Stack Copy - archive.ncarb.org
examines tools and technologies that are driving real time big data analytics Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science …
THe HaDooP STaCk: New ParaDIGm for BIG DaTa …
In this article, we first present the new paradigm (in particular, the Hadoop stack) that is required for big data storage and processing. After that, we describe how to optimize the Hadoop …
The Data Science Technology Stack - NITRD
•Examples from the largest scale commercial big data systems. •My personal top five recommendations for critical technology investments for large data systems
Big Data, Hadoop, Map-Reduce - Middle East Technical …
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed …
Big Data Technology Stack [PDF] - www2.x-plane.com
big data technology stack: Big Data SMACK Raul Estrada, Isaac Ruiz, 2016-09-29 Learn how to integrate full-stack open source big data architecture and to choose the correct …
Big Data Technology Stack - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Big Data Architecture Patterns - BigRio
there are complex data architecture challenges both with designing the new “Big Data” stack as well as with integrating it with existing transactional and warehousing technologies. This paper …
Big Data - content.e-bookshelf.de
1 Introduction to the World of Big Data 1 1.1 Understanding Big Data 1 1.2 Evolution of Big Data 2 1.3 Failure of Traditional Database in Handling Big Data 3 1.4 3Vs of Big Data 4 1.5 Sources of …
Choosing a big data technology stack for digital marketing
Use this to develop a set of key decision vectors for choosing an appropriate digital technology stack for your data. Lay out a series of the most common digital marketing data use cases and …
International Journal of Engineering and Advanced …
This paper assesses the complete education on big data and various characterizes of big data like volume, velocity and variety. Survey about Big Data technologies along with their architecture …
Big Data Technology Stack (book) - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
InsideBIGDATA Guide to Big Data for Finance - ICDST
effectively exploit the big data technology stack, advanced statistical modeling, and predictive analytics in support of real-time decision making across business channels and operations will …
Big Data Technology Stack (PDF) - bubetech.com
Big Data Processing Using Spark in Cloud Mamta Mittal,Valentina E. Balas,Lalit Mohan Goyal,Raghvendra Kumar,2018-06-16 The book describes the emergence of big data …
Big Data Technology Stack - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Impact of the big data technology stack in digital marketing …
influence the performances itself identified as handling huge volumes of data, range of data modeling, data integration concession, supporting algorithmic queries, real-time support, …
Intro to Big Data - گروه آموزشی و پژوهشی سیب
Big Data Processing Frameworks Comparison •There are plenty of options for processing within a big data system. For batch-only workloads that are not time-sensitive, Hadoop is a good …
INtroduCtIoN to BIg data: INfrastruCture aNd NetworkINg …
Aug 27, 2012 · Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five …
MONITORING AND TROUBLESHOOTING BIG DATA …
As Big Data applications become increasingly integral to modern business operations, ensuring their seamless performance is critical. Monitoring and troubleshooting these applications pose …
Big Data Technology Stack (Download Only) - archive.ncarb.org
data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of …
Big Data Technology Stack Copy - archive.ncarb.org
Big Data Technology Stack: Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science technology stack and perform good data science with …
Big Data Technology Stack Copy - archive.ncarb.org
examines tools and technologies that are driving real time big data analytics Practical Data Science Andreas François Vermeulen,2018-02-21 Learn how to build a data science …
