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25.Big Data with Hadoop and Cloud Computing

25.Big Data with Hadoop and Cloud Computing

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25.Big Data with Hadoop and Cloud Computing

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    1. Big Data with Hadoop and Cloud Computing

    2. “Big Data Processing” relevant for Enterprises Big Data used to be discarded or un-analyzed & archived. Loss of information, insight, and prospects to extract new value. How Big Data is beneficial? Energy companies - Geophysical analysis. Science and medicine - Empiricism is growing than experimentation Disney – Customer behavior patterns across its stores, and theme parks Pursuit of a “Competitive Advantage” is the driving factor for Enterprises Data mining (Log processing, click stream analysis, similarity algorithms, etc.), Financial simulation (Monte Carlo simulation), File processing (resize jpegs), Web indexing

    3. Cloud Computing ~ brings economy to Big Data Processing Big Data Processing can be implemented by HPC & Cloud. 1) HPC implementation is very costly w.r.t. CAPEX & OPEX. 2) Cloud Computing is efficient because of its paper use nature. MapReduce programming model is used for processing big data sets. Pig, Hive, Hadoop, … are used for Big data Processing Pig - SQL-like operations that apply to datasets., Hive - Perform SQL-like data analysis on data Hadoop - processes vast amounts of data; (Focal point) Use EC2 instances to analyze “Big Data” in Amazon IaaS. Amazon MapReduce reduces complex setup & Magt.

    4. Cost Comparison of Alternatives

    5. Future Direction Current Experiments & Identified areas Social network analysis Managing Data center Collective Intelligence - Algorithms and Visualization techniques Predictive analytics Accelerators Exploration Apache Whirr - Cloud-neutral way to run services Apache Mahout - Scalable machine learning library Cascading - Distributed computing framework HAMA - define and execute fault tolerant data processing workflows Exploration of LAMP-like stack for Big Data aggregation, processing and analytics

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