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SQL on Big Data
Learn various commercial and open source products that perform SQL on Big Data platforms. You will understand the architectures of the various SQL engines being used and how the tools work internally in terms of execution, data movement, latency, scalability, performance, and system requirements. This book consolidates in one place solutions to the challenges associated with the requirements of speed, scalability, and the variety of operations needed for data integration and SQL operations. After discussing the history of the how and why of SQL on Big Data, the book provides in-depth insight into the products, architectures, and innovations happening in this rapidly evolving space. SQL on Big Data  discusses in detail the innovations happening, the capabilities on the horizon, and how they solve the issues of performance and scalability and the ability to handle different data types. The book covers how SQL on Big Data engines are permeating the OLTP, OLAP, and Operational analytics space and the rapidly evolving HTAP systems. You will learn the details of: Batch Architectures Understand the internals and how the existing Hive engine is built and how it is evolving continually to support new features and provide lower latency on queries Interactive Architectures Understanding how SQL engines are architected to support low latency on large data sets Streaming Architectures Understanding how SQL engines are architected to support queries on data in motion using in-memory and lock-free data structures Operational Architectures Understanding how SQL engines are architected for transactional and operational systems to support transactions on Big Data platforms Innovative Architectures Explore the rapidly evolving newer SQL engines on Big Data with innovative ideas and concepts Who This Book Is For: Business analysts, BI engineers, developers, data scientists and architects, and quality assurance professionals
Category: Computers. ISBN: 9781484222461

Big Data Revolution
Exploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist. Companies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology. Examine the major emerging patterns in Big Data Consider the debate surrounding the ethical use of data Recognize patterns and improve personal and organizational performance Make more informed decisions with quantifiable results In an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.
Category: Technology. ISBN: 9781118943717

Praxishandbuch Big Data
Dieses Praxishandbuch bietet einen berblick der mglichen Anwendungsfelder und der rechtlichen Rahmenbedingungen von Big Data im Unternehmen. Im ersten Teil wird gezeigt, wie Entscheidungsprozesse mit Daten fundiert werden knnen und welche Anwendungsmglichkeiten in verschiedenen Branchen denkbar sind. Der zweite Teil behandelt die rechtlichen Aspekte von Big Data. Die Autoren geben praktische Empfehlungen, wie Big Data-Anwendungen nach geltendem Recht umgesetzt werden knnen und dabei den technischen und organisatorischen Aufwand so gering wie mglich zu halten. Auch wird erlutert, wie Unternehmen ihre Datenbestnde schtzen knnen. Der dritte Teil beschftigt sich mit den technischen Voraussetzungen von Big Data-Anwendungen.  
Category: Business. ISBN: 9783658072889

Big Data Fundamentals
This text should be required reading for everyone in contemporary business. --Peter Woodhull, CEO, Modus21 The one book that clearly describes and links Big Data concepts to business utility. --Dr. Christopher Starr, PhD Simply, this is the best Big Data book on the market! --Sam Rostam, Cascadian IT Group of the most contemporary approaches I've seen to Big Data fundamentals... --Joshua M. Davis, PhD The Definitive Plain-English Guide to Big Data for Business and Technology Professionals Big Data Fundamentals provides a pragmatic, no-nonsense introduction to Big Data. Best-selling IT author Thomas Erl and his team clearly explain key Big Data concepts, theory and terminology, as well as fundamental technologies and techniques. All coverage is supported with case study examples and numerous simple diagrams. The authors begin by explaining how Big Data can propel an organization forward by solving a spectrum of previously intractable business problems. Next, they demystify key analysis techniques and technologies and show how a Big Data solution environment can be built and integrated to offer competitive advantages. Discovering Big Data's fundamental concepts and what makes it different from previous forms of data analysis and data science Understanding the business motivations and drivers behind Big Data adoption, from operational improvements through innovation Planning strategic, business-driven Big Data initiatives Addressing considerations such as data management, governance, and security Recognizing the 5 V characteristics of datasets in Big Data environments: volume, velocity, variety, veracity, and value Clarifying Big Data's relationships with OLTP, OLAP, ETL, data warehouses, and data marts Working with Big Data in structured, unstructured, semi-structured, and metadata formats Increasing value by integrating Big Data resources with corporate performance monitoring Understanding how Big Data leverages distributed and parallel processing Using NoSQL and other technologies to meet Big Data's distinct data processing requirements Leveraging statistical approaches of quantitative and qualitative analysis Applying computational analysis methods, including machine learning
Category: Computers. ISBN: 9780134291079

Scalable Big Data Architecture
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance. Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications , which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution. When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time. This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on. Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data. Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
Category: Computers. ISBN: 9781484213278

Big Data Analytics
This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome;  graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
Category: Business. ISBN: 9788132236269

Advances in Big Data
The book offers a timely snapshot of neural network technologies as a significant component of big data analytics platforms. It promotes new advances and research directions in efficient and innovative algorithmic approaches to analyzing big data (e.g. deep networks, nature-inspired and brain-inspired algorithms); implementations on different computing platforms (e.g. neuromorphic, graphics processing units (GPUs), clouds, clusters); and big data analytics applications to solve real-world problems (e.g. weather prediction, transportation, energy management). The book, which reports on the second edition of the INNS Conference on Big Data, held on October 2325, 2016, in Thessaloniki, Greece, depicts an interesting collaborative adventure of neural networks with big data and other learning technologies.
Category: Computers. ISBN: 9783319478975

Big Visual Data Analysis
This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoorscene classification, and outdoor scene layout estimation. It is illustrated with numerous naturaland synthetic color images,and extensive statistical analysis is provided to help readers visualize big visualdata distribution and the associatedproblems. Although therehas been some research on big visual data analysis, little workhas been published on big image data distribution analysis using the modernstatistical approach described in thisbook. By presenting a complete methodology on big visual data analysis withthree illustrative scene comprehensionproblems, it provides ageneric framework that canbe applied to other big visual data analysis tasks.
Category: Technology. ISBN: 9789811006296

Big Data SMACK
Learn how to integrate full-stack open source big data architecture and to choose the correct technologyScala/Spark, Mesos, Akka, Cassandra, and Kafkain 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
Category: Computers. ISBN: 9781484221747

Privacy and Big Data
Much of what constitutes Big Data is information about us. Through our online activities, we leave an easy-to-follow trail of digital footprints that reveal who we are, what we buy, where we go, and much more. This eye-opening book explores the raging privacy debate over the use of personal data, with one undeniable conclusion: once data's been collected, we have absolutely no control over who uses it or how it is used. Personal data is the hottest commodity on the market todaytruly more valuable than gold. We are the asset that every company, industry, non-profit, and government wants. Privacy and Big Data introduces you to the players in the personal data game, and explains the stark differences in how the U.S., Europe, and the rest of the world approach the privacy issue. You'll learn about: Collectors: social networking titans that collect, share, and sell user data Users: marketing organizations, government agencies, and many others Data markets: companies that aggregate and sell datasets to anyone Regulators: governments with one policy for commercial data use, and another for providing security
Category: Computers. ISBN: 9781449305000
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