Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives (Hardcover)
暫譯: 超越 Hadoop 的大數據分析:使用 Storm、Spark 及其他 Hadoop 替代方案的即時應用 (精裝版)

Vijay Srinivas Agneeswaran

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商品描述

Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning.

 

When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: 

  • Spark, the next generation in-memory computing technology from UC Berkeley
  • Storm, the parallel real-time Big Data analytics technology from Twitter
  • GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo)

Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics.

 

Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

商品描述(中文翻譯)

掌握替代的 Big Data 技術,這些技術能做到 Hadoop 無法實現的功能:即時分析和迭代機器學習。

當大多數技術專業人士今天想到 Big Data 分析時,他們會想到 Hadoop。但有許多尖端應用並不適合 Hadoop,特別是即時分析和需要使用迭代機器學習算法的情境。幸運的是,已經開發出幾種強大的新技術,專門用於這些用例。《Big Data Analytics Beyond Hadoop》是第一本專門設計來幫助您邁出超越 Hadoop 的下一步的指南。Vijay Srinivas Agneeswaran 博士詳細介紹了突破性的 Berkeley Data Analysis Stack (BDAS),包括其動機、設計、架構、Mesos 集群管理、性能等。他提供了現實的用例和最新的示例代碼,包括:

- Spark,來自 UC Berkeley 的下一代內存計算技術
- Storm,來自 Twitter 的並行即時 Big Data 分析技術
- GraphLab,來自 CMU 和華盛頓大學的下一代圖形處理範式(並與 Pregel 和 Piccolo 等替代方案進行比較)

Halo 還提供了架構和設計指導,以及將機器學習算法擴展到 Big Data 的代碼草圖,並在即時中實現它們。他最後預覽了新興趨勢,包括即時視頻分析、SDN,甚至 Big Data 的治理、安全和隱私問題。他指出了一些引人注目的初創公司和新的研究可能性,包括 BDAS 擴展和尖端的模型驅動分析。

《Big Data Analytics Beyond Hadoop》是每個希望達到 Big Data 分析前沿並保持在那裡的人的不可或缺的資源:實踐者、架構師、程序員、數據科學家、研究人員、初創企業家和高級學生。