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While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types. Use guidelines to evaluate and select data management solutions. Reduce risk related to technology, your team, and vague requirements. Explore system interface design using APIs, REST, and pub/sub systems. Choose the right distributed storage system for your big data system. Plan and implement metadata collections for your data architectureUse data pipelines to ensure data integrity from source to final storage. Evaluate the attributes of various engines for processing the data you collect.

[Purpose/Significance] Scientific and technical (S&T) literature data resources are characterized with wide coverage, large quantity, many types, fast update and strong timeliness. In order to improve the effect and security of S&T literature data management, this paper studies the S&T literature management system based on the data life cycle model. [Method/Process] This paper explores the management mode of S&T documents, constructs the life cycle system of S&T documents based on the data management process, and expounds the data management tools and methods from the stages of data creation, data storage, data pre-processing, data calculation, data service, data archiving and data destruction. In the data creation stage, specific data access forms are formulated for different sources and data types, and personalized data creation tools are built to receive data completely. In the data storage stage, a unified document metadata storage system is developed by analyzing the characteristics and shortcomings of various types of data, so as to better explain and organize scientific and technological document data. In the data pre-processing stage, various tools are built to realize the formatting pre-processing, parsing, conversion, structuring and other operations of various types of data. In the data computing stage, data enrichment processing, entity relationship extraction and knowledge graph construction are mainly completed. Data provides services through a unified service interface. Data archiving completes data archiving and saving. In the data destruction phase, unnecessary data is safely destroyed. [Results/Conclusions] In this paper, the management and practice based on the life cycle of S&T literature were first carried out based on the core data set Web Of Science BP data , and then explored from the seven phases of creation, storage, pre-processing, calculation, service, archiving and destruction. Finally, based on the DAMA data quality evaluation principle, the comprehensive evaluation and evaluation of the data management effect were carried out from the six dimensions of integrity, uniqueness, real-time, validity, accuracy and consistency. The receiving integrity of data was 100%, and the non-null integrity of data was 59.75%. The uniqueness of data reached 99.23%. The real time of data was controllable. The validity of data met the constraint conditions. The accuracy of the data reached 100%. The consistency of data reached 90%. It basically solved the problem that data can be effectively managed and applied in each life cycle stage. Finally, the management model was verified to take effect and achieve desirable service effect.

Recent rapid developments in information and network technology have profoundly influenced manufacturing research and its application. However, the product’s functionality and complexity of the manufacturing environments are intensifying, and organizations need to sustain the advantage of huge competitiveness in the markets. Hence, collaborative manufacturing, along with computer-based distributed management, is essential to enable effective decisions and to increase the market. A comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework and to shed light on the future research avenues. In this review paper, the use of technology and management by means of collaborative and cloud manufacturing process and big data in networked manufacturing system have been discussed. A systematic review of research papers is done to draw conclusion and moreover, future research opportunities for collaborative manufacturing system were highlighted and discussed so that manufacturing enterprises can take maximum benefit.

\"Learn to use Apache Pig to develop lightweight big data applications easily and quickly. This book shows you many optimization techniques and covers every context where Pig is used in big data analytics. Beginning Apache Pig shows you how Pig is easy to learn and requires relatively little time to develop big data applications. The book is divided into four parts: the complete features of Apache Pig; integration with other tools; how to solve complex business problems; and optimization of tools. You'll discover topics such as MapReduce and why it cannot meet every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be created; submitting Pig jobs using Hue; and working with Oozie. You'll also see how to extend the framework by writing UDFs and custom load, store, and filter functions. Finally you'll cover different optimization techniques such as gathering statistics about a Pig script, joining strategies, parallelism, and the role of data formats in good performance. What You Will Learn* Use all the features of Apache Pig* Integrate Apache Pig with other tools* Extend Apache Pig* Optimize Pig Latin code* Solve different use cases for Pig LatinWho This Book Is ForAll levels of IT professionals: architects, big data enthusiasts, engineers, developers, and big data administrators.\"-- Provided by publisher.

PurposeThis study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.Design/methodology/approachThe target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.FindingsFindings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.Practical implicationsThis study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.Originality/valueThe proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.

The study of big data analytics (BDA) methods for the data-driven industries is gaining research attention and implementation in today’s industrial activities, business intelligence, and rapidly changing the perception of industrial revolutions. The uniqueness of big data and BDA has created unprecedented new research calls to solve data generation, storage, visualization, and processing challenges. There are significant gaps in knowledge for researchers and practitioners on the right information and BDA tools to extract knowledge in large significant industrial data that could help to handle big data formats. Notwithstanding various research efforts and scholarly studies that have been proposed recently on big data analytic processes for industrial performance improvements. Comprehensive review and systematic data-driven analysis, comparison, and rigorous evaluation of methods, data sources, applications, major challenges, and appropriate solutions are still lacking. To fill this gap, this paper makes the following contributions: presents an all-inclusive survey of current trends of BDA tools, methods, their strengths, and weaknesses. Identify and discuss data sources and real-life applications where BDA have potential impacts. Other main contributions of this paper include the identification of BDA challenges and solutions, and future research prospects that require further attention by researchers. This study provides an insightful recommendation that could assist researchers, industrial practitioners, big data providers, and governments in the area of BDA on the challenges of the current BDA methods, and solutions that would alleviate these challenges.

Residents in Boston, Massachusetts, are automatically reporting potholes and road hazards via their smartphones. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. But how do these organizations and municipalities do it? 'Too Big To Ignore' explains how organisations can reap massive benefits from analyzing today's new and emerging types of data.

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.