Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015

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Codemotion Rome 2015 - I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.

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Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015

  1. 1. Hands On Big Data: Getting Started With NoSQL And Hadoop Mario Cartia mario@big-data.ninja
  2. 2. Big Data Facts •  Google processes about 20Pb (E+15 bytes) of data each day •  About 5Eb (Exabytes, E+18 bytes) of data in the world. 90% generated over last 2 years •  Wearable computing and IoT…
  3. 3. Big Data: 3V Model •  Big Data it’s not only about volume – Volume >= Petabytes, not Gigabytes – Variety Structured and unstructured data – Velocity Real-time or near real-time
  4. 4. Big Data Risk
  5. 5. Big Data Opportunity
  6. 6. Big Data Facts
  7. 7. Big Data Success Stories Amazon.com, a pioneer of targeted advertising became a big data user when Greg Linden, one of its software engineers realized the potential of book reviewing from the average results of their in-house review project When Amazon compared the results of the computer sales against the in house reviews, the results were much better for the data- derived material, and revolutionized e- commerce
  8. 8. Big Data Success Stories Google Flu Trends is a web service operated by Google. It provides estimates of influenza activity for more than 25 countries. By aggregating Google search queries, it attempts to make accurate predictions about flu activity In the 2009 flu pandemic Google Flu Trends tracked information about flu in the United States. In February 2010, the CDC identified influenza cases spiking in the mid-Atlantic region of the United States. However, Google’s data of search queries about flu symptoms was able to show that same spike two weeks prior to the CDC report being released
  9. 9. Big Data Success Stories reCAPTCHA is a user-dialogue system originally developed by Luis von Ahn, Ben Maurer, Colin McMillen, David Abraham and Manuel Blum at Carnegie Mellon University's main Pittsburgh campus, and acquired by Google in September 2009 The reCAPTCHA service supplies subscribing websites with images of words that optical character recognition (OCR) software has been unable to read. The subscribing websites present these images for humans to decipher as CAPTCHA words, as part of their normal validation procedures. They then return the results to the reCAPTCHA service, which sends the results to the digitization projects Secondary data usage
  10. 10. Big Data Techniques Statistics Data Warehouse Data Visualization Data Mining Prediction Machine Learning Advanced Analytics Correlation Analysis Business Intelligence
  11. 11. The Traditional Approach ETL: Extract, Transform, Load •  Extracts data from outside sources •  Transforms it to fit operational needs, which can include quality levels •  Loads it into the end target (database, operational data store, data mart or data warehouse) Does it fit “big data” needs?
  12. 12. Hadoop Basics Apache Hadoop is an open-source software framework for distributed storage and distributed processing of Big Data on clusters of commodity hardware
  13. 13. Hadoop Basics Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Cutting, who was working at Yahoo! at the time named it after his son's toy elephant
  14. 14. Hadoop 1 vs. Hadoop 2
  15. 15. Hadoop Distributions
  16. 16. Hadoop Market
  17. 17. Hadoop vs. RDBMS
  18. 18. From RDBMS to NoSQL A NoSQL (often interpreted as Not Only SQL) database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases
  19. 19. From RDBMS to NoSQL Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. The data structure (e.g. key-value, graph, or document) differs from the RDBMS, and therefore some operations are faster in NoSQL and some in RDBMS
  20. 20. NoSQL Approaches Most popular NoSQL database types •  Document (MongoDB, CouchDB, Clusterpoint, Couchbase, MarkLogic, etc.) •  Key-value (Redis, MemcacheDB, Dynamo, FoundationDB, Riak, FairCom c-treeACE, Aerospike, etc.) •  Column (Accumulo, Cassandra, Druid, HBase, Vertica, etc.) •  Graph (Allegro, Neo4J, InfiniteGraph, OrientDB, Virtuoso, Stardog, etc.)
  21. 21. NoSQL Approaches
  22. 22. NoSQL How To Choose(Brewer) CAP theorem (Brewer)
  23. 23. Hadoop Architecture Overview
  24. 24. Hadoop Core Components
  25. 25. MapReduce Model •  MapReduce is a programming model, and an associated implementation, for processing and generating large data sets with a parallel, distributed algorithm on a cluster •  The model is inspired by the map and reduce functions commonly used in functional programming, although their purpose in the MapReduce framework is not the same as in their original forms
  26. 26. MapReduce Paper
  27. 27. MapReduce Overview •  Map step: Each worker node applies the map() function to the local data, and writes the output to a temporary storage. A master node orchestrates that for redundant copies of input data, only one is processed •  Shuffle step: Worker nodes redistribute data based on the output keys (produced by the map() function), such that all data belonging to one key is located on the same worker node •  Reduce step: Worker nodes now process each group of output data, per key, in parallel
  28. 28. Map Reduce: A really simple introduction Dear <Your Name>, As you know we are building the blogging platform blogger2.com, I need some statistics. I need to find out, Acorss all blogs ever wrriten on blogger.com, how many times 1 character words occur(like 'a', 'I'), How many times two character words occur (like 'be', 'is').. and so on till how many times do ten character words occur. I know its a really big job. So, I will assign, all 50,000 employees working in our company to work with you on this for a week. I am going on a vacation for a week, and its really important that I've this when I return. Good luck. regds, The CEO (src: http://ksat.me/map-reduce-a-really-simple-introduction-kloudo/)
  29. 29. Map Reduce: A really simple introduction The next day, You stand with a mike on the dias before 50,000 and proclaim. For a week, you will all be divided into many groups: •  The Mappers (tens of Thousands of people will be in this group) •  The Grouper (Assume just one guy for now) •  The Reducers ( Around 10 of em.) and.. •  The Master (That’s you)
  30. 30. Map Reduce: A really simple introduction •  Each mapper will get a set of 50 blog urls and really Big sheet of paper. Each one of you need to go to each of that url. and for each word in those blogs, write one line on the paper. The format of that line should be the number of characters in the word, then a commna, and then the actual word •  For example, if you find the word “a”, you write “1,a”, in a new line in your paper. since the word “a” has only 1 character. If you find the word “hello”, you write “5,hello” on the new line
  31. 31. Map Reduce: A really simple introduction Each take 4 days. So, After 4 days, your sheet might look like this •  “1,a” •  “5,hello” •  “2,if” •  .. and a million more lines At the end of the 4th day. each one of you will give your sheet completely filled to the Grouper
  32. 32. Map Reduce: A really simple introduction •  I will give you 10 papers. The first paper will be marked 1, the second paper will be marked 2, and so on, till 10 •  You collect the output from mappers and for each line in the mapper’s sheet, if it says “1,”, your write the on sheet 1, if it says “2, ”, you write it on sheet two •  For example, if the first line of a mapper’s sheet says “1,a”, you write “a” on sheet 1. if it says “2,if”, your write “if” on sheet 2. If it says “5,hello”, you write hello on sheet 5
  33. 33. Map Reduce: A really simple introduction So at the end of your work, the 10 sheets you have might look like this •  Sheet 1: a, a ,a , I, I , i, a, i, i, i…. millions more •  Sheet 2: if, of, it, of, of, if, at, im, is,is, of, of … millions more •  Sheet 3 :the, the, and, for, met, bet, the, the, and, … millions more •  .. •  Sheet 10: …… once you are done, you distribute, each sheet to one reducer. For example sheet 1 goes to reducer 1, sheet 2 goes to reducer 2 and so on.
  34. 34. Map Reduce: A really simple introduction •  Each one of you gets one sheet from the grouper. For each sheet you count the number of words written on it and write it in big bold letters on the back side of the paper. •  For ex, if you are reducer 2 you get sheet 2 from the grouper that looks like this: “Sheet 2: if, of, it, of, of, if, at, im, is,is, of, of …” •  You count the number of words on that sheet, say the number of words is 28838380044, You write it on the back side of the paper , in big bold letters and give it to the Master
  35. 35. Map Reduce: A really simple introduction You essentially did map reduce. The greatest advantage in your approach was this: •  The mappers can work independently •  The reducers can work independently •  The grouper can work really fast, because, he din’t have to do any counting of words, all the had to do was to look at the first number and put that word in the appropriate sheet The process can be easily applied to other kinds of problems
  36. 36. Map Reduce: formal definition The Map and Reduce functions of MapReduce are both defined with respect to data structured in (key, value) pairs. Map takes one pair of data with a type in one data domain, and returns a list of pairs in a different domain: •  Map(k1 ,v1) → list(k2, v2)
  37. 37. Map Reduce: formal definition The Map function is applied in parallel to every pair in the input dataset This produces a list of pairs for each call After that, the MapReduce framework collects all pairs with the same key from all lists and groups them together, creating one group for each key
  38. 38. Map Reduce: formal definition The Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain: •  Reduce(k2, list (v2)) → list(v3) Each Reduce call typically produces either one value v3 or an empty return, though one call is allowed to return more than one value. The returns of all calls are collected as the desired result list
  39. 39. MapReduce job example package org.myorg; import java.io.IOException; … public class WordCount { public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); output.collect(word, one); } } }
  40. 40. MapReduce job example public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } }
  41. 41. MapReduce job example public static void main(String[] args) throws Exception { JobConf conf = new JobConf(WordCount.class); conf.setJobName("wordcount"); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(IntWritable.class); conf.setMapperClass(Map.class); conf.setCombinerClass(Reduce.class); conf.setReducerClass(Reduce.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); FileInputFormat.setInputPaths(conf, new Path(args[0])); FileOutputFormat.setOutputPath(conf, new Path(args[1])); JobClient.runJob(conf); } }
  42. 42. Machine Learning Machine learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions
  43. 43. Machine Learning Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and optimization, which deliver methods, theory and application domains to the field
  44. 44. Machine Learning Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining
  45. 45. Machine Learning Examples
  46. 46. Machine Learning Examples
  47. 47. Machine Learning Tools Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification
  48. 48. Machine Learning Tools
  49. 49. Data Visualization Studies show the brain processes images 60,000x faster than text. The final step in your big data analytics workflow, the big data analytics visualization is a visual representation of the insights gained from your analysis
  50. 50. Data Visualization Tools
  51. 51. Data Visualization Tools

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