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“Introducing Hadoop on Azure:

hello Map-Reduce!”. “Introducing Hadoop on Azure:. Joe Hummel, PhD Visiting Researcher: U. of California, Irvine Adjunct Professor: U. of Illinois, Chicago & Loyola U., Chicago. Materials: http ://www.joehummel.net/downloads.html Email: joe@joehummel.net. A little history….

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“Introducing Hadoop on Azure:

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  1. hello Map-Reduce!” “Introducing Hadoop on Azure: Joe Hummel, PhD Visiting Researcher: U. of California, IrvineAdjunct Professor: U. of Illinois, Chicago & Loyola U., Chicago Materials: http://www.joehummel.net/downloads.html Email: joe@joehummel.net

  2. A little history… • Map-Reduce is from functional programming // function returns 1 if i is prime, 0 if not: letisPrime(i) = ... // sums 2 numbers: letsum(x, y) = return x + y // count the number of primes in 1..N: letcountPrimes(N) = let L = [ 1 .. N ] // [ 1, 2, 3, 4, 5, 6, ... ] let T = mapisPrime L // [ 0, 1, 1, 0, 1, 0, ... ] let count = reducesum T // 42 return count Hadoop on Azure

  3. A little more history… • Hadoop: • Created by to drive internet search • Parallelism • Data partitioning • Fault tolerance BIG Data page hits

  4. Hadoop today • Freely-available framework for big data • http://hadoop.apache.org/ • Based on concept of Map-Reduce: mapfunction reduce intermediate results BIG data Map Map Reduce R Map Map . . . . . .

  5. Workflow Data Map Map Map [ <key1,value>, <key4,value>, <key2,value>, … ] Sort Sort Sort [ <key1,value>, <key1,value>, … ] Merge [ <key1, [value,value,…]>, <key2, [value,value,…]>, … ] Reduce R [ <key1, value>, <key2, value>… ]

  6. Data set for demo • We’ll be working with Chicago crime data… • https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2 • http://www.cityofchicago.org/city/en/narr/foia/CityData.html 1 GB 5M rows

  7. Goal? • Compute top-10 crimes… IUCR Count 0486 366903 0820 308074 . . . 0890 166916 IUCR = Illinois Uniform Crime Codes https://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

  8. Demo • Hadoop on Azure… • // Javascript version: • varmap= function (key, value, context) • { • var values = value.split(","); • context.write(values[4], 1); • }; • varreduce= function (key, values, context) • { • var sum = 0; • while (values.hasNext()) • { • sum += parseInt(values.next()); • } • context.write(key, sum); • }; 0486 366903 0820 308074 . . . Hadoop on Azure

  9. Hadoop++ • Rich ecosystem around Hadoop • Pig • Hive • HBASE • … • // interactive PIG with explicit Map-Reduce functions: • pig.from("CC-from-2001.txt"). • mapReduce("IUCR-Count.js", "IUCR, Count:long"). • orderBy("Count DESC"). • take(10). • to("output-from-2001") • // interactive PIG without explicit Map-Reduce: • schema = "ID,CaseNumber,Date,Block,IUCR,..." • pig.from("CC-from-2001.txt", schema). • groupBy("IUCR"). • select("group, SUM($1.Count"). • orderBy("Count DESC"). • take(10). • to("output-from-2001") Hadoop on Azure

  10. Hadoop on Azure • Microsoft is offering free access to Hadoop • Request invitation @ http://www.hadooponazure.com/ • Hadoopconnector for Excel • Process data using Hadoop, analyze/visualize using Excel Hadoop on Azure

  11. PowerPivot • Freely-available plugin for Excel 2010 • http://www.powerpivot.com/ • Turns Excel into an in-memory database • More precisely, turns spreadsheet into an OLAP cube • Note: • If you have 32-bit Excel, install 32-bit PowerPivot • If you have 64-bit Excel, install 64-bit PowerPivot • GBs of data will require 64-bit • [ How to tell what version of Excel you have? File menu, help… ] Big Data Processing, Cheap

  12. Demo • PowerPivot… • Install • PowerPivot menu • PowerPivot Window • Get Data... • PivotTable… Big Data Processing, Cheap

  13. Compare and contrast Big Data Processing, Cheap

  14. That’s it! Big Data Processing, Cheap

  15. Thank you for attending • Presenter: Joe Hummel • Email: joe@joehummel.net • Materials: http://www.joehummel.net/downloads.html • Keep an eye for final release of: • Hadoop on Azure • Hadoop on Windows • PowerView plugin for Excel 2013 Big Data Processing, Cheap

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