| Description |
1 online resource (1 volume) : illustrations |
| Series |
FT Press Analytics Ser. |
|
FT Press Analytics Ser.
|
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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 BerkeleyStorm, the parallel real-time Big Data analytics technology from TwitterGraphLab, 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. |
| Contents |
1. Introduction: Why look beyond Hadoop map-reduce? -- 2. What is the Berkeley data analytics stack (BDAS)? -- 3. Realizing machine learning algorithms with spark -- 4. Realizing machine learning algorithms in real time -- 5. Graph processing paradigms -- 6. Conclusions: big data analytics beyond Hadoop map-reduce -- Appendix A. Code sketches. |
| Subject |
Apache Hadoop.
|
|
Apache Hadoop |
|
Electronic data processing.
|
|
Big data.
|
|
Data mining.
|
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Business intelligence.
|
|
Information storage and retrieval systems.
|
|
Data Mining |
|
Information Systems |
|
Données volumineuses. |
|
Exploration de données (Informatique) |
|
Systèmes d'information. |
|
Big data |
|
Business intelligence |
|
Data mining |
|
Electronic data processing |
|
Information storage and retrieval systems |
| ISBN |
9780133838220 (electronic bk.) |
|
0133838226 (electronic bk.) |
|
0133837947 |
|
9780133837940 |
| Standard No. |
9780133837940 |
|
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