Big Data Fundamentals: Concepts, Drivers & Techniques (Paperback)
暫譯: 大數據基礎：概念、驅動因素與技術 (平裝本)

Thomas Erl, Wajid Khattak, Paul Buhler

Big Data Fundamentals: Concepts, Drivers & Techniques (Paperback)

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商品描述

“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

商品描述(中文翻譯)

“這本書應該是當代商業人士的必讀書籍。”
--彼得·伍德哈爾，Modus21 首席執行官

“這是一本清楚描述並將大數據概念與商業效用連結的書。”
--克里斯多福·斯塔爾博士

“簡單來說，這是市場上最好的大數據書籍！”
--山姆·羅斯坦，Cascadian IT Group

“……這是我見過的最現代化的大數據基礎方法之一……”
--約書亞·M·戴維斯博士

商業與技術專業人士的大數據權威簡明指南

大數據基礎提供了一個務實且不拖泥帶水的大數據入門。暢銷IT作者托馬斯·厄爾及其團隊清楚地解釋了關鍵的大數據概念、理論和術語，以及基本技術和方法。所有內容都以案例研究和多個簡單的圖示作為支持。

作者首先解釋了大數據如何推動組織前進，解決一系列以往難以處理的商業問題。接著，他們揭開了關鍵分析技術和技術的神秘面紗，並展示了如何構建和整合大數據解決方案環境，以提供競爭優勢。


  • 發現大數據的基本概念以及它與以往數據分析和數據科學的不同之處

  • 理解推動大數據採用的商業動機和驅動因素，從操作改進到創新

  • 規劃以商業為驅動的大數據戰略計劃

  • 考慮數據管理、治理和安全等問題

  • 認識大數據環境中數據集的五個“V”特徵：數量（volume）、速度（velocity）、多樣性（variety）、真實性（veracity）和價值（value）

  • 澄清大數據與OLTP、OLAP、ETL、數據倉庫和數據集市的關係

  • 處理結構化、非結構化、半結構化和元數據格式的大數據

  • 通過將大數據資源與企業績效監控整合來增加價值

  • 理解大數據如何利用分散式和並行處理

  • 使用NoSQL和其他技術來滿足大數據獨特的數據處理需求

  • 利用定量和定性分析的統計方法

  • 應用計算分析方法，包括機器學習

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