Download e-book for kindle: Big Data. Principles and Paradigms by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi

By Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi

ISBN-10: 0128053941

ISBN-13: 9780128053942

ISBN-10: 0128093463

ISBN-13: 9780128093467

Big info: ideas and Paradigms captures the cutting-edge study at the architectural features, applied sciences, and purposes of massive info. The e-book identifies power destiny instructions and applied sciences that facilitate perception into a variety of clinical, enterprise, and patron applications.

To support detect monstrous Data’s complete power, the e-book addresses a number of demanding situations, delivering the conceptual and technological suggestions for tackling them. those demanding situations contain life-cycle information administration, large-scale garage, versatile processing infrastructure, facts modeling, scalable desktop studying, info research algorithms, sampling suggestions, and privateness and moral issues.

  • Covers computational systems assisting substantial info applications
  • Addresses key ideas underlying colossal facts computing
  • Examines key advancements aiding subsequent iteration large info platforms
  • Explores the demanding situations in colossal facts computing and how one can triumph over them
  • Contains professional participants from either academia and industry

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In: Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation, April 25–27; 2012. [71] Karau H, Konwinski A, Wendell P, Zaharia M. Learning spark. USA: O’Reilly Media Inc; 2015. [72] Ewen S, Schelter S, Tzoumas K, Warneke D, Markl V. Iterative parallel processing with stratosphere an inside look. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD 13; 2013. p. 1053–6. [73] Tzoumas K. net/FlinkForward/ k-tzoumas-s-ewen-flink-forward-keynote.

P. 1–12. [16] Schonberger VM, Cukier K. Big data, a revolution that will transform how we live, work and think. USA: Houghton Mifflin Harcourt Publishing Company; 2013. p. 10 [17] de Montjoy Y-A, et al. Unique in the shopping mall: on the re-identifiability of credit card metadata. Am Assoc Adv Sci Sci 2015;347(6221):536–9. [18] Redman TC. Data doesn’t speak for itself. Harvard business review; 2014 April. 29. [19] Gomes L. Machine-learning maestro Michael Jordan on the delusions of big data and other huge engineering efforts.

2. 3. 4. 5. 6.  9). It also has many APIs to interface with other BDA applications. Many ASF incubation projects (such as Spark and Flink) can replace MapReduce, but it would be too costly to substitute the entire Hadoop framework. 8 ­ML + CC → BDA AND GUIDELINES We discussed the role of ML, CC, and Hadoop-like systems. We see that ML and CC are the two most important components of BDA. If there are no advances in ML and CC, BDA could not be implemented or operated cost effectively. Of course, BDA needs a good understanding of application Table 7 Guidelines for BDA 3 Aspects 9Vs Fit for BDA Not Fit for BDA Data Volume • Datasets do not fit into one node (eg, PB–EB size datasets) • Bringing computing to the data • Not only SQL • Collection and discovery of datasets from different data sources (eg, M2M, WSN, RFID, SCADA, SQL, and NoSQL) • Schemaa on read • Data agility • Interactive and dynamic data stream • Datasets are not clean • Models construction need many “Whatifs” for fidelity issues • Rely on archived data for reliability and credibility • Heterogeneous dataset • Dynamic or flexible schemas • Numerous variables of dataset (eg, > 50 variables) • Require independent and transparent criteria to verify the result of BDA (eg, GFT) • Solving strategic problems that have long-term consequences (eg, competitive advantages, integrity, excellence, sustainability, success, and cost leadership) • Leveraging business values from all data sources • Ask for an answer • Search for strategic insight • Large-scale computing needing high fault tolerance • Scale-out • High percentage of parallel and distributed processing workload • Dataset can be fit into one node • Bringing data to the computing node Variety Velocity Statistics Veracity Variability Validity Business Intelligence Value Verdict Visibility Other Aspects • One type workload (RDBMS or SQL) • Single data source • Schema on write • • • • • Traditional stable environment Static dataset Dataset is relatively clean Model construction is relatively simple Require live data • Homogeneous dataset • Fixed schema • Few variables of dataset • Simple and straightforward approach to verify the result of data mining • Routine issues for a short term • Exploring business value from single source • Ask for the answer • Search for temporary solutions • Fault tolerance may not be essential • Scale-up • Percentage of serial processing workloads is higher “Schema-on-Read” means a table or a set of statements is not predefined.

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Big Data. Principles and Paradigms by Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi

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