1st Birmingham Big Data Science Group meetup
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1st Birmingham Big Data Science Group meetup

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    1st Birmingham Big Data Science Group meetup 1st Birmingham Big Data Science Group meetup Presentation Transcript

    • Welcome to the Birmingham Big Data Science Group (BIDS)
      Faizan Javed
      5/25/2011
      Intermark Group
      Sponsor: Intermark Group
    • BIDS Stats
      Founded April 10, 2011
      9 members (and counting..)
      Founder: Faizan Javed, Co-Founder: QasimIjaz
      Online presence:
      Meetup.com for co-ordinatingmeetups:
      http://www.meetup.com/bham-bids
      Also on (for related articles and announcements):
      LinkedIn: http://www.linkedin.com/groups/Birmingham-Big-Data-Science-Group-3865219
      Facebook:http://www.facebook.com/home.php?sk=group_202221519811444
    • Agenda
      What is Big Data?
      Quick overview of related technologies:
      Large-scale distributed systems and platforms
      NoSQL data stores
      Intelligent algorithms/web-mining/information retrieval techniques
      Highly-scalable systems
    • What is Big Data?
      More people connected to the internet
      Social media explosion (Web 2.0): Facebook, Twitter, etc.
      Huge volumes of data being collected: sensors, mobile devices, machine-to-machine communications, social media and retail sites web logs for browsing patterns
      “Big” in Big Data is relative:  today's "big" is certainly tomorrow's "medium" and next week's "small.“
      “Big Data" is when the size of the data itself becomes part of the problem. Going from Gigabytes to Petabytes!http://radar.oreilly.com/2010/06/what-is-data-science.html
    • Big Data, Big Numbers McKinsey report, May 2011: http://www.mckinsey.com/mgi/publications/big_data/index.asp
    • Why care about big data?
      Deep analysis of data can be a competitive advantage.
      More data  easier to find consistent patterns
      More data usually beats better algorithms
      Ex 1: Predict customer preferences and target ads on an ecommerce website.
      Ex 2: Improve search quality.
      Ex 3: Bank risk modeling (aggregate customer activity from different lines of businesses)
      http://blog.mikepearce.net/2010/08/18/10-hadoop-able-problems-a-summary/
      http://www.ft.com/intl/cms/s/0/64095dba-7cd5-11e0-994d-00144feabdc0.html#axzz1NHn8icSC
      Key point: “Many different sources” & “unstructured data”
    • Big Players on the Big Data Scene
      The Government http://us1.campaign-archive1.com/?u=4cb4c08d876d7481bbc4bc70f&id=6889126aef
    • The need for new techniques
      Traditional “relational” techniques breakdown at scale.
      Solutions:
      NoSQL databases: Cassandra, Hbase, Riak, etc
      Large-scale “commodity” scale-out distributed computing techniques: MapReduce/Hadoop, Percolator, etc
      Analytics platforms: IBM BigInsight, EMC GreenPlum
    • The NoSQL revolutionhttp://www.infoq.com/news/2011/04/newsql
    • Prominent NoSQL database users
      Cassandra: Facebook, Twitter, Rackspace, Reddit, Digg.com
      Riak: Mozilla, Ask.com, Comcast
      Voldemort: LinkedIn
      MongoDB: Foursquare, Etsy, bit.ly, Intuit
      Hbase: Stumbleupon, Twitter, Infolinks, Adobe, Meetup.com,
    • Hadoop-based SMAQ stackhttp://radar.oreilly.com/2010/09/the-smaq-stack-for-big-data.html
      public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable>
      {
      public void reduce(Text key, Iterable<IntWritable> values, Context context)
      throws IOException, InterruptedException
      {
      int sum = 0;
      for (IntWritableval : values)
      {
      sum += val.get();
      }
      context.write(key, new IntWritable(sum));
      }
      }
    • Hadoop-based SMAQ stack
      Hadoop comes with HDFS – Hadoop Distributed File Sytem.
      Can be used alongside various NoSQL systems (Hbase most common)
    • Hadoop-based SMAQ stack
      Pig (yahoo)
      input = LOAD 'input/sentences.txt' USING TextLoader();
      words = FOREACH input GENERATE FLATTEN(TOKENIZE($0)); grouped = GROUP words BY $0;
      counts = FOREACH grouped GENERATE group, COUNT(words); ordered = ORDER counts BY $0; STORE ordered INTO 'output/wordCount' USING PigStorage();
      Hive (facebook)
      INSERT OVERWRITE TABLE xyz_com_page_views SELECT page_views.* FROM page_views WHERE page_views.date >= '2008-03-01' AND page_views.date <= '2008-03-31' AND page_views.referrer_url like '%xyz.com';
    • Next-generation systems: going beyond MapReduce/Hadoophttp://www.nytimes.com/external/gigaom/2010/10/23/23gigaom-beyond-hadoop-next-generation-big-data-architectu-81730.html
      Mostly Google and Yahoo innovations.
      Percolator – “real-time” MapReduce. Powers Google Instant.
      Dremel – superfast “Hive” to interact with large-datasets. Inhouse-Google.
      Pregel– highly efficient graph computing for analyzing social graphs. In-house Google. Open-source projects available.
      Megastore- scalable NoSQL like system with ACID semantics but lower consistency across partitions. In-house Google.
      Next-gen Hadoop at Yahoo: enhanced scalability (going beyond 4000 clusters), support for multiple programming paradigms, enhanced cluster utilization.
    • Intelligent Web & machine learning
      Recommendation systems, data/web mining, natural language processing
      Recommendation systems:
      A type of collaborative filtering/information retrieval technique.
      Uses user profiles, ratings, browsing habits to recommend items not yet considered.
      First made famous in the commercial arena by Amazon.com
    • Amazon.com & Netflix recommendation systems
    • Foursquare (3/2011) and Google Places (5/2011)http://engineering.foursquare.com/2011/03/22/building-a-recommendation-engine-foursquare-style/ http://places.blogspot.com/2011/05/discover-more-places-youll-like-based.html
    • Hot area!Netflix and Overstock.com competitions
    • Search Engines (Google, Bing, Wolfram, Lucene/Nutch, etc)
    • Search innovations @ LinkedInhttp://thenoisychannel.com/2010/01/31/linkedin-search-a-look-beneath-the-hood/http://blog.linkedin.com/2009/12/14/linkedin-faceted-search/
      Uses open-source Luceneproject for social graph search and real-time indexing and searching.
      Dynamic filters automatically generated based on your query results!
    • Conclusion
      Big Data is a very challenging and promising area
      Can be used to get a competitive advantage
      Usually bring about advances in computer science
      Vast area of topics: NoSQL systems, large-scale distributed computing systems, highly scalable web system designs
      Machine learning techniques: search engines, recommender systems