By Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi
Big facts: ideas and Paradigms captures the state of the art examine at the architectural points, applied sciences, and purposes of massive info. The booklet identifies capability destiny instructions and applied sciences that facilitate perception into a variety of medical, enterprise, and customer applications.
To aid detect enormous Data’s complete power, the ebook addresses various demanding situations, providing the conceptual and technological suggestions for tackling them. those demanding situations comprise life-cycle information administration, large-scale garage, versatile processing infrastructure, info modeling, scalable computer studying, information research algorithms, sampling concepts, and privateness and moral issues.
- Covers computational structures aiding colossal information applications
- Addresses key rules underlying large info computing
- Examines key advancements aiding subsequent iteration massive info platforms
- Explores the demanding situations in enormous info computing and how one can triumph over them
- Contains professional members from either academia and industry
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16. Generally, Spark is a fast- and general-purpose computation platform based on large clusters. In contrast to MapReduce that is basically designed for a web crawler, indexing system, and limited ML, Spark includes SQL, interactive query, data stream, graph, and ML analytic functions in its computation platform. 17). Spark consists of seven major elements: Spark core of data engine, Spark cluster manager (includes Hadoop, Apache Mesos, and built-in Standalone cluster manger), Spark SQL, Spark streaming, Spark Machine Learning Library, Spark GraphX, and Spark programming tools.
In contrast to MapReduce that is basically designed for a web crawler, indexing system, and limited ML, Spark includes SQL, interactive query, data stream, graph, and ML analytic functions in its computation platform. 17). Spark consists of seven major elements: Spark core of data engine, Spark cluster manager (includes Hadoop, Apache Mesos, and built-in Standalone cluster manger), Spark SQL, Spark streaming, Spark Machine Learning Library, Spark GraphX, and Spark programming tools. 18) that are capable of supporting MapReduce-like processing requirements.
23. [49] Witten IH, Frank E. Data mining practical machine learning tools and techniques. 3rd ed. USA: Elsevier Inc; 2011. [50] Mitchell TM. Machine learning. USA: McGraw-Hill Science; 1997. p. 2. [51] Murphy KP. Machine learning, a probabilistic perspective. USA: The MIT Press; 2012. [52] Bishop CM. Pattern recognition and machine learning. USA: Springer; 2006. [53] Nisan N, Schocken S. The element of computing systems building a modern computer from first principles. USA: MIT press; 2005. p. 57–8.