Building Big Data Applications Book

Building Big Data Applications

  • Author : Krish Krishnan
  • Publisher : Academic Press
  • Release Date : 2019-11-15
  • Genre: Computers
  • Pages : 242
  • ISBN 10 : 9780128158043

Building Big Data Applications Excerpt :

Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.). Explores various ways to leverage Big Data by effectively integrating it into the data warehouse Includes real-world case studies which clearly demonstrate Big Data technologies Provides insights on how to optimize current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements

Designing Data Intensive Applications Book
Score: 5
From 3 Ratings

Designing Data Intensive Applications

  • Author : Martin Kleppmann
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2017-03-16
  • Genre: Uncategoriezed
  • Pages : 616
  • ISBN 10 : 9781491903100

Designing Data Intensive Applications Excerpt :

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Software Architecture for Big Data and the Cloud Book

Software Architecture for Big Data and the Cloud

  • Author : Ivan Mistrik
  • Publisher : Morgan Kaufmann
  • Release Date : 2017-06-12
  • Genre: Computers
  • Pages : 470
  • ISBN 10 : 9780128093382

Software Architecture for Big Data and the Cloud Excerpt :

Software Architecture for Big Data and the Cloud is designed to be a single resource that brings together research on how software architectures can solve the challenges imposed by building big data software systems. The challenges of big data on the software architecture can relate to scale, security, integrity, performance, concurrency, parallelism, and dependability, amongst others. Big data handling requires rethinking architectural solutions to meet functional and non-functional requirements related to volume, variety and velocity. The book's editors have varied and complementary backgrounds in requirements and architecture, specifically in software architectures for cloud and big data, as well as expertise in software engineering for cloud and big data. This book brings together work across different disciplines in software engineering, including work expanded from conference tracks and workshops led by the editors. Discusses systematic and disciplined approaches to building software architectures for cloud and big data with state-of-the-art methods and techniques Presents case studies involving enterprise, business, and government service deployment of big data applications Shares guidance on theory, frameworks, methodologies, and architecture for cloud and big data

Big Data Architect   s Handbook Book

Big Data Architect s Handbook

  • Author : Syed Muhammad Fahad Akhtar
  • Publisher : Packt Publishing Ltd
  • Release Date : 2018-06-21
  • Genre: Computers
  • Pages : 486
  • ISBN 10 : 9781788836388

Big Data Architect s Handbook Excerpt :

A comprehensive end-to-end guide that gives hands-on practice in big data and Artificial Intelligence Key Features Learn to build and run a big data application with sample code Explore examples to implement activities that a big data architect performs Use Machine Learning and AI for structured and unstructured data Book Description The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action. What you will learn Learn Hadoop Ecosystem and Apache projects Understand, compare NoSQL database and essential software architecture Cloud infrastructure design considerations for big data Explore application scenario of big data tools for daily activities Learn to analyze and visualize results to uncover valuable insights Build and run a big data application with sample code from end to end Apply Machine Learning and AI to perform big data intelligence Practice the daily activi

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast Book

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast

  • Author : Federico Divina
  • Publisher : MDPI
  • Release Date : 2021-08-30
  • Genre: Technology & Engineering
  • Pages : 100
  • ISBN 10 : 9783036508627

Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast Excerpt :

The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.

Big Data Computing Book

Big Data Computing

  • Author : Rajendra Akerkar
  • Publisher : CRC Press
  • Release Date : 2013-12-05
  • Genre: Business & Economics
  • Pages : 564
  • ISBN 10 : 9781466578388

Big Data Computing Excerpt :

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries

Big Data Applications for Improving Library Services Book

Big Data Applications for Improving Library Services

  • Author : Sangeeta N. Dhamdhere
  • Publisher : Unknown
  • Release Date : 2020
  • Genre: Academic libraries
  • Pages : null
  • ISBN 10 : 1799830500

Big Data Applications for Improving Library Services Excerpt :

"This book explores the application of big data in library services"--

Big Data Applications in Industry 4  0 Book

Big Data Applications in Industry 4 0

  • Author : P. Kaliraj
  • Publisher : CRC Press
  • Release Date : 2022
  • Genre: Business & Economics
  • Pages : 422
  • ISBN 10 : 1003175880

Big Data Applications in Industry 4 0 Excerpt :

"Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including artificial intelligence (AI), Big Data analytics, Internet-of-Things (IoT) and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development. Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real world problems. The books features: An introduction to data science and the types of data analytics methods accessible today: An overview of data integration concepts, methodologies, and solutions. A general framework of forecasting principles and applications as well as basic forecasting models including naèive, moving average, and exponential smoothing models. A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies. The application of Industry 4.0 and Big Data in the field of education. The features, prospects, and significant role of Big Data in banking industry, as well as various use cases of Big Data in banking, finance s

Understanding China through Big Data Book

Understanding China through Big Data

  • Author : Yunsong Chen
  • Publisher : Routledge
  • Release Date : 2021-07-15
  • Genre: Social Science
  • Pages : 272
  • ISBN 10 : 9781000412345

Understanding China through Big Data Excerpt :

Chen, He and Yan present a range of applications of multiple-source big data to core areas of contemporary sociology, demonstrating how a theory-guided approach to macrosociology can help to understand social change in China, especially where traditional approaches are limited by constrained and biased data. In each chapter of the book, the authors highlight an application of theory-guided macrosociology that has the potential to reinvigorate an ambitious, open-minded and bold approach to sociological research. These include social stratification, social networks, medical care, and online behaviours among many others. This research approach focuses on macro-level social process and phenomena by using quantitative models to statistically test for associations and causalities suggested by a clearly hypothesised social theory. By deploying theory-oriented macrosociology where it can best assure macro-level robustness and reliability, big data applications can be more relevant to and guided by social theory. An essential read for sociologists with an interest in quantitative and macro-scale research methods, which also provides fascinating insights into Chinese society as a demonstration of the utility of its methodology.

Agile Data Science Book

Agile Data Science

  • Author : Russell Jurney
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2013-10-15
  • Genre: COMPUTERS
  • Pages : 178
  • ISBN 10 : 9781449326920

Agile Data Science Excerpt :

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Big Data Bootcamp Book

Big Data Bootcamp

  • Author : David Feinleib
  • Publisher : Apress
  • Release Date : 2014-09-26
  • Genre: Business & Economics
  • Pages : 244
  • ISBN 10 : 9781484200407

Big Data Bootcamp Excerpt :

Investors and technology gurus have called big data one of the most important trends to come along in decades. Big Data Bootcamp explains what big data is and how you can use it in your company to become one of tomorrow’s market leaders. Along the way, it explains the very latest technologies, companies, and advancements. Big data holds the keys to delivering better customer service, offering more attractive products, and unlocking innovation. That’s why, to remain competitive, every organization should become a big data company. It’s also why every manager and technology professional should become knowledgeable about big data and how it is transforming not just their own industries but the global economy. And that knowledge is just what this book delivers. It explains components of big data like Hadoop and NoSQL databases; how big data is compiled, queried, and analyzed; how to create a big data application; and the business sectors ripe for big data-inspired products and services like retail, healthcare, finance, and education. Best of all, your guide is David Feinleib, renowned entrepreneur, venture capitalist, and author of Why Startups Fail. Feinleib’s Big Data Landscape, a market map featured and explained in the book, is an industry benchmark that has been viewed more than 150,000 times and is used as a reference by VMWare, Dell, Intel, the U.S. Government Accountability Office, and many other organizations. Feinleib also explains: • Why every businessperson needs to understand the fundamentals of big data or get run over by those who do • How big data differs from traditional database management systems • How to create and run a big data project • The technical details powering the big data revolution Whether you’re a Fortune 500 executive or the proprietor of a restaurant or web design studio, Big Data Bootcamp will explain how you can take full advantage of new technologies to transform your company and your career.

Big Data and Analytics Applications in Government Book

Big Data and Analytics Applications in Government

  • Author : Gregory Richards
  • Publisher : CRC Press
  • Release Date : 2017-09-18
  • Genre: Business & Economics
  • Pages : 219
  • ISBN 10 : 9781351649629

Big Data and Analytics Applications in Government Excerpt :

Within this context, big data analytics (BDA) can be an important tool given that many analytic techniques within the big data world have been created specifically to deal with complexity and rapidly changing conditions. The important task for public sector organizations is to liberate analytics from narrow scientific silos and expand it across internally to reap maximum benefit across their portfolios of programs. This book highlights contextual factors important to better situating the use of BDA within government organizations and demonstrates the wide range of applications of different BDA techniques. It emphasizes the importance of leadership and organizational practices that can improve performance. It explains that BDA initiatives should not be bolted on but should be integrated into the organization’s performance management processes. Equally important, the book includes chapters that demonstrate the diversity of factors that need to be managed to launch and sustain BDA initiatives in public sector organizations.

Big Data Analytics Book

Big Data Analytics

  • Author : Frank Ohlhorst
  • Publisher : John Wiley & Sons
  • Release Date : 2012-11-28
  • Genre: Business & Economics
  • Pages : 160
  • ISBN 10 : 9781118147597

Big Data Analytics Excerpt :

Takes an in-depth look at the financial value of big data analytics and offers tools and best practices for working with big data. Once the domain of large on-line retailers such as eBay and Amazon, big data is now accessible by businesses of all sizes and across industries.

Building Machine Learning Powered Applications Book

Building Machine Learning Powered Applications

  • Author : Emmanuel Ameisen
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2020-01-21
  • Genre: Computers
  • Pages : 260
  • ISBN 10 : 9781492045069

Building Machine Learning Powered Applications Excerpt :

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment