20 Best Big Data Books (2023 Update)

A widely recognized authority on big data, he is the author of over a hundred articles and eight books, of which the most recent is delete: the virtue of forgetting in the digital age. [au photo] kenneth cukier is the data editor of the economist and a prominent commentator on developments in big data. his writings on business and economics have appeared in foreign affairs, the new york times, the financial times, and elsewhere.

Summary – big data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. it describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. – purchase of the print book includes a free ebook in pdf, kindle, and e – pub formats from manning publications. – about the book – web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. these applications require architectures built around clusters of machines to store and process data of any size, or speed. fortunately, scale and simplicity are not mutually exclusive. – big data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. this book presents the lambda architecture, a scalable, easy-to-understand approach that can be built and run by a small team. you'll explore the theory of big data systems and how to implement them in practice. in addition to discovering a general framework for processing big data, you'll learn specific technologies like hadoop, storm, and no – sql databases. – this book requires no previous exposure to large-scale data analysis or no – sql tools. familiarity with traditional databases is helpful. – what's inside – introduction to big data systems – real-time processing of web-scale data – tools like hadoop, cassandra, and storm – extensions to traditional database skills – about the authors – nathan marz is the creator of apache storm and the originator of the lambda architecture for big data systems. james warren is an analytics architect with a background in machine learning and scientific computing. – table of contents – a new paradigm for big data – part 1 batch layerdata model for big data – data model for big data: illustration – data storage on the batch layer – data storage on the batch layer: illustration – batch layer – batch layer: illustration – an example batch layer: architecture and algorithms – an example batch layer: implementation – part 2 serving layerserving layer – serving layer: illustration – part 3 speed layerrealtime views – realtime views: illustration – queuing and stream processing – queuing and stream processing: illustration – micro-batch stream processing – micro-batch stream processing: illustration – lambda architecture in depth

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.

If you want to learn about big data, then keep reading… do you want to understand what big data is all about, but you don't get all the hype? are you intrigued by the idea of building a career around big data and data science, but you just don't understand it? if you know nothing about big data now, you will come away with a good overview of the subject and how it integrates into the other hot technologies of the day widely adopted by business, such as artificial intelligence, predictive analytics, and machine learning. even if you don't intend to get directly involved in big data, understanding it will be very important in the coming years. it is one of the most important phenomena to hit in many years. in this book, you will learn: what is big data, and why is it important? the five v's behind big data – how big data is already impacting your life, and where big data may be headed – how big data and your everyday devices and appliances will come together in unexpected ways via the internet of things – how companies and governments are using predictive analytics to get ahead of the competition or improve service – how big data is used for fraud detection – how big data can train intelligent computer systems – the many ways large corporations are benefiting from big data and the tools that use it like machine learning, ai, and predictive analytics – upcoming trends in big data that are sure to have a large impact on your future artificial intelligence, and how big data drives its development – what machine learning is and how it is tied to big data – insights into how big data impacts privacy issues – the pros and cons regarding big data – and much, much more! if you want to learn about this new, exciting, and rapidly developing technology, then download this book right now! you will not be baffled by big data any longer, and you will understand the behavior of large companies far better than you ever did before.

This book deals with the analysis of data protection issues raised in the context of the general data protection regulation – rgpd, considering the data generated through the advancement of information and communication technologies, namely those characterized by a large volume per unit of time, called big data. with the digital metamorphosis of recent years, society as a whole lives its reflections in everyday life and law is one of the areas that should also adapt to the digitally established big data scenario. related to the theme, starting with the forms of data production, from the historical part to the current sophisticated means regarding such production and the important points for achieving the most effective protection possible, covering from the fundamental principles that govern protection of those data, advancing later to the rights of data subjects deserving of effective protection at the time legal relations, as well as the new measures that the general data protection regulation introduces on the matter.

Do you feel you are falling behind on big data?… or do you see everyone using it to get ahead of you? are you tempted to finally see what the hype is all about? do you feel ignorant, unclear what big data is, and why it is the future? are you terrified of ending up old having wasted years not taking advantage of your data? if you stay in the dark, you'll never take advantage of this opportunity. is this positive for you? big data: a beginner's guide to using data science for business gets you up to speed fast, including an exploration of the history and future of big data. this is a book of knowledge and doesn't just tell you to try harder. life rewards those who take matters into their own hands, and this book is where to start. big data is full of real-life examples of how big data impacts people just like you. these examples are backed up countless data expert studies, all which will arm you with a knowledge primed for success with using big data immediately. easy-to-implement small changes and practical takeaways for immediate action. what happens if you ignore big data? * learn the history of big data. * why should you care about becoming an expert in big data? * what could you achieve with tips in the right direction * the consequences of ignoring big data in your business how will you learn to compete in this data-driven age? * identify the keys to mastering big data * which tools are used to win at this game * tricks for getting your feet on the ground today * how to develop the competency you are lacking what happens when you don't let life pass you by? * never wonder "what if" you could have made millions with big data! * wake up every day with high energy and desire * inspire yourself and others to become experts at big data. * fulfill your destiny and start getting paid for this knowledge. find out how to let go of your lack of knowledge and take flight towards being a big data expert, period. create the business of your dreams around big data. try big data: a beginner's guide to using data science for business today by clicking the buy now button at the top right of this page! p.s.

Data science and big data analytics is about harnessing the power of data for new insights. the book covers the breadth of activities and methods and tools that data scientists use. the content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

"turn yourself into a data head. you'll become a more valuable employee and make your organization more successful." thomas h. davenport, research fellow, author of competing on analytics, big data @ work, and the ai advantage you've heard the hype around data—now get the facts. in becoming a data head: how to think, speak, and understand data science, statistics, and machine learning, award-winning data scientists alex gutman and jordan goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. you'll learn how to: think statistically and understand the role variation plays in your life and decision making speak intelligently and ask the right questions about the statistics and results you encounter in the workplace understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence avoid common pitfalls when working with and interpreting data becoming a data head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. the authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. anyone can become a data head—an active participant in data science, statistics, and machine learning. whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

"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 "…one 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

Get command of your organizational big data using the power of data science and analytics – key features – a perfect companion to boost your big data storing, processing, analyzing skills to help you take informed business decisions – work with the best tools such as apache hadoop, r, python, and spark for nosql platforms to perform massive online analyses – get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for big data – book description – big data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. crafting an enterprise-scale cost-efficient big data and machine learning solution to uncover insights and value from your organization's data is a challenge. today, with hundreds of new big data systems, machine learning packages, and bi tools, selecting the right combination of technologies is an even greater challenge. – this book will help you do that. with the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate big data and data science platforms. you will get hands-on exposure to hadoop and spark, build machine learning dashboards using r and r shiny, create web-based apps using nosql databases such as mongo – db, and even learn how to write r code for neural networks. – by the end of the book, you will have a very clear and concrete understanding of what big data analytics means, how it drives revenues for organizations, and how you can develop your own big data analytics solution using the different tools and methods articulatedin this book. – what you will learn – get a 360-degree view of the world of big data, data science, and machine learning – go through a broad range of technical and business big data analytics topics that caters to the interests of technical experts as well as corporate it executives – get hands-on experience with industry-standard big data and machine learning tools such as hadoop, spark, mongo – db, kdb+, and rcreate production-grade machine learning bi dashboards using r and r shiny with step-by-step instructions – learn how to combine open-source big data, machine learning, an bi tools to create low-cost business analytics applications – understand corporate strategies for successful big data and data science projects – go beyond general-purpose analytics to develop cutting-edge big data applications using emerging technologies – who this book is for: the book is intended for existing and aspiring big data professionals who wish to become the go-to person in their organization when it comes to big data architecture, analytics, and governance. while no prior knowledge of big data or related technologies is assumed, it will be helpful to have some programming experience.

Summary practical data science with r, second edition takes a practice-oriented approach to explaining basic principles in the ever expanding field of data science. you’ll jump right to real-world use cases as you apply the r programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. purchase of the print book includes a free ebook in pdf, kindle, and epub formats from manning publications. about the technology evidence-based decisions are crucial to success. applying the right data analysis techniques to your carefully curated business data helps you make accurate predictions, identify trends, and spot trouble in advance. the r data analysis platform provides the tools you need to tackle day-to-day data analysis and machine learning tasks efficiently and effectively. about the book practical data science with r, second edition is a task-based tutorial that leads readers through dozens of useful, data analysis practices using the r language. by concentrating on the most important tasks you’ll face on the job, this friendly guide is comfortable both for business analysts and data scientists. because data is only useful if it can be understood, you’ll also find fantastic tips for organizing and presenting data in tables, as well as snappy visualizations. what's inside statistical analysis for business pros effective data presentation the most useful r tools interpreting complicated predictive models about the reader you’ll need to be comfortable with basic statistics and have an introductory knowledge of r or another high-level programming language. about the author nina zumel and john mount founded a san francisco–based data science consulting firm. both hold phds from carnegie mellon university and blog on statistics, probability, and computer science.

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. – sql 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

Many of today's businesses find themselves caught in a snarl of internal data, paralyzed by internal silos, and executing antiquated marketing approaches. as a result, consumers are losing patience, shareholders are clamoring for growth and differentiation, and marketers are left struggling to untangle the massive mess. meanwhile, valuable information about consumer preferences continues to accumulate and overwhelm decision makers. it's time to tame the beast named big data. big data marketing provides a strategic road map for executives who want to clear the chaos and start driving competitive advantage and top line growth. using real-world examples, non-technical language, additional downloadable resources, and a healthy dose of humor, big data marketing will help you discover the remedy offered by data-driven marketing with a five-step plan for tackling big data insights. you'll discover: how marketers can use data to learn what they need to know how to make metrics your mantra the five fundamental components of a successful strategy strategies to drive marketing relevance and return on marketing investment (romi) approaches to managing marketing spend—a company's largest variable expenditure how to drive value through relevant marketing proven ways to elevate customer experiences learn how to better understand the market and advance your overall strategy and marketing techniques. leverage these insights to ensure your business's success.

Learn big data from the ground up with this complete and up-to-date resource from leaders in the field big data: concepts, technology, and architecture delivers a comprehensive treatment of big data tools, terminology, and technology perfectly suited to a wide range of business professionals, academic researchers, and students. you’ll learn about the creation of structured, unstructured, and semi-structured data, data storage solutions, traditional database solutions like sql, data processing, data analytics, machine learning, and data mining. you’ll also discover how specific technologies like apache hadoop, sqoop, and flume work. big data also covers the central topic of big data visualization with tableau, and you’ll learn how to create scatter plots, histograms, bar, line, and pie charts with that software. accessibly organized, big data includes illuminating case studies throughout the material, showing you how the included concepts have been applied in real-world settings. some of those concepts include: the common challenges facing big data technology and technologists, like data heterogeneity and incompleteness, data volume and velocity, storage limitations, and privacy concerns relational and non-relational databases, like rdbms, nosql, and newsql databases virtualizing big data through encapsulation, partitioning, and isolating, as well as big data server virtualization apache software, including hadoop, cassandra, avro, pig, mahout, oozie, and hive the big data analytics lifecycle, including business case evaluation, data preparation, extraction, transformation, analysis, and visualization perfect for data scientists, data engineers, and database managers, big data also belongs on the bookshelves of business intelligence analysts who are required to make decisions based on large volumes of information. executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

Big data is one of the most popular buzzwords in technology industry today. organizations worldwide have realized the value of the immense volume of data available and are trying their best to manage, analyse and unleash the power of data to build strategies and develop a competitive edge. at the same time, the advent of the technology has led to the evolution of a variety of new and enhanced job roles. the objective of this book is to create a new breed of versatile big data analysts and developers, who are thoroughly conversant with the basic and advanced analytic techniques for manipulating and analysing data. big data is one of the most popular buzzwords in technology industry today. organizations worldwide have realized the value of the immense volume of data available and are trying their best to manage, analyse and unleash the power of data to build strategies and develop a competitive edge. at the same time, the advent of the technology has led to the evolution of a variety of new and enhanced job roles. the objective of this book is to create a new breed of versatile big data analysts and developers, who are thoroughly conversant with the basic and advanced analytic techniques for manipulating and analysing data. dt editorial services has seized the market of computer books, bringing excellent content in software development to the fore. the team is committed to excellenceexcellence in the quality of content, excellence in the dedication of its authors and editors, excellence in the attention to detail, and excellence in understanding the needs of its readers.

Do you want to expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data centric issues? exploring this book could be a step in the right direction… 2 comprehensive manuscripts in 1 book data science: what the best data scientists know about data analytics, data mining, statistics, machine learning, and big data – that you don't data science for business: predictive modeling, data mining, data analytics, data warehousing, data visualization, regression analysis, database querying, and machine learning for beginners part one of this book will cover topics such as: what data science is what it takes to become an expert in data science best data mining techniques to apply in data data visualization logistic regression data engineering machine learning big data analytics and much more part 2 of this book will discuss the following topics: how big data works and why it is so important how to do an explorative data analysis working with data mining how to mine text to get the data some amazing machine learning algorithms to help with data science how to do data modeling data visualization how to use data science to help your business grow tips to help you get started with data science and much, much more so if you want to learn more about data science, click "buy now"

So many people dream of becoming their own boss or succeeding in their chosen profession, and with the resources available today, more entrepreneurs and professionals are achieving great success! however, success should be defined for the long term, and as opportunities start to grow, so does the competition. getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. many people make the mistake of making plans but having no follow-through. this is where analytics comes in. don't you wish to have the power to know what your target consumers are thinking? won't you want to have a preview of what future trends to expect in the market you are in? well, this book is just the one you need. this book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time. the contents of this book are designed to help you use data to your advantage to enhance business outcomes!

Used in stanford's cs102 big data (spring 2017) course. want to get started on data science? this book has been written in layman's terms as a gentle introduction to data science and its algorithms. each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application.

Deliver enterprise data analytics success by following prashanth's prescriptive and practical techniques. this book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. it is intended for anyone who has a stake and interest in deriving insights from data analytics. the three key differentiating aspects of this book are: practicality . prescriptive and practical guidance. completeness . holistic view across the four key data analytics domains – data management, data engineering, data science, and data visualization. neutrality . technologically agnostic without any reference to commercial analytics products and technologies. dr. southekal proves why he is one of the leading thinkers on data and analytics today. 'analytics best practices' is an indispensable guide for business leaders and those looking to get into the analytics field on the nuances, challenges, and immense opportunities with data. douglas b. laney, principal, data & analytics strategy, caserta, and author of "infonomics" dr. southekal's book is a treasure trove of best practices and practical examples from the field of data analytics. upen varanasi, ceo and co-founder, riversand technologies inc. what i like about this book is its focus on real-world best practices with an extensive set of practical tips and tricks. it provides an important bridge between the data management and business operations sides of a business. michael wade, professor of innovation and strategy, imd business school prashanth's book is accessible and practical – an excellent guide for corporate leaders who want to produce meaningful business results from the use of data and analytics to create true business value. ram kumar, executive head and senior vice president, quantium dr. prashanth southekal has created a practical guidebook for realizing business value from data and analytics investments. highly recommended. randy bean, founder and ceo, newvantage partners, and contributing author for forbes and harvard business review prashanth's second book starts with the right title – it is always about business value. the practices explored here will help anyone interested to achi …

Written by renowned data science experts foster provost and tom fawcett, data science for business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. this guide also helps you understand the many data-mining techniques in use today. – based on an mba course provost has taught at new york university over the past ten years, data science for business provides examples of real-world business problems to illustrate these principles. you'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. you'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. – understand how data science fits in your organization—and how you can use it for competitive advantage – treat data as a business asset that requires careful investment if you're to gain real value – approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way – learn general concepts for actually extracting knowledge from data – apply data science principles when interviewing data science job candidates