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Use Commodity Hardware• “Cheap” Commodity Server Hardware– No need for super-computers, use commodity unreliablehardware– Not desktopsNOTBUT12Hadoop System Principles• Scale-Out rather than Scale-Up• Bring code to data rather than data to code• Deal with failures – they are common• Abstract complexity of distributed andconcurrent applications13
Scale-Out Instead of Scale-Up• It is harder and more expensive to scale-up– Add additional resources to an existing node (CPU, RAM)– Moore’s Law can’t keep up with data growth– New units must be purchased if required resources can not beadded– Also known as scale vertically• Scale-Out– Add more nodes/machines to an existing distributedapplication– Software Layer is designed for node additions or removal– Hadoop takes this approach - A set of nodes are bondedtogether as a single distributed system– Very easy to scale down as well14Code to Data• Traditional data processing architecture– nodes are broken up into separate processing and storagenodes connected by high-capacity link– Many data-intensive applications are not CPU demandingcausing bottlenecks in networkProcessingNodeProcessingNodeLoad DataSave ResultsLoad DataSave ResultsStorageNodeStorageNodeRisk of bottleneck
Page 1 and 2: © 2012 coreservlets.com and Dima M
Page 3 and 4: Hadoop• Existing tools were not d
Page 5: Hadoop Clusterclient client clientH
Page 9 and 10: Abstract Complexity• Hadoop abstr
Page 11 and 12: Comparisons to RDBMS (Continued)22
Page 13 and 14: Hadoop Eco System• To start build
Page 15 and 16: Hadoop Eco System• Your organizat
Page 17 and 18: Cloudera Distribution forHadoop (CD
Page 19 and 20: Resources: BooksHadoop: The Definit
Page 21 and 22: © 2012 coreservlets.com and Dima M

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