Business of Big Data
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Business of Big Data






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Business of Big Data Business of Big Data Presentation Transcript

  • 1 Big Data the next frontierRVC Seminar Leonid ZhukovMoscow, 08/02/2013 Professor Higher School of Economics
  • 2Big data+ Graph of terms popularity
  • 3McKinsey, May 2011
  • 4Headlines Data driven business Data democratization Data scientists
  • 5The White House+ $200M initiative+ NSF: core techniques+ NIH: 1000 genomes+ DOE: advanced computing+ DOD: data to decisions+ USGS: Earth system
  • 6Gartner Hype Cycle
  • 7 Market Forecast + Venture money invested (Reuters):+ Market forecasts: + 2009 - $1.1B + IDC: 2015 - $16.9B + 2010 - $1.53B + Gartner: 2016- $55B + 2011 - $2.47B
  • 8Big Data Revenue 2012 + Big Business: + IBM + HP + Oracle + Teradata + EMC
  • 9Big Data Vendors! + Hadoop: + Cloudera + MapR Techonologies + HortonWorks
  • 10Forrester Wave
  • What is big data 11+ Big data: + “Data you can’t process by traditional tools” + “A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex and diverse types of data.” + “Refers to a collection of tools, techniques and technologies for working with data productively, at any scale.”
  • 12What is Big data + 3V + Volume: petabytes (1000TB) to exabytes (1000PB) + Variety: structured, semi-structured, unstructured + Velocity: Tb/s data streams + Requires distributed processing + Big data = storage + processing + Big data = Hadoop (not only)
  • 13Big data Glossary+ Hadoop, MapReduce, Hive, Pig, Cascading, HBase, Hypertable, Cassandra, Flume, Sqoop, Mongo, Voldemort, Storm, Kafka, Drill, Dremmel, Impala, Zookeeper, Ambari, Oozi, Yarn, Redis, Rajak, Pregel, Gremlin, Giraph, Solr, Lucene, R, Mahout, Weka,
  • 14How big is Big?+ Google + 24 PB data processed daily+ Twitter + 340 mln daily tweets + 1.6 bln search queries + 7 TB added daily+ Facebook + 750 mln users + 12 TB daily daily content + 2.7 bln “likes” and comments daily
  • 15Sources of Big Data
  • 16Supercomputing+ National Labs, Universities, Military+ Processing power, flops, MPI+ Parallel computing: + Cray, IBM SP, SGI + Beowulf cluster (Linux commodity)
  • 17New realities+ Yahoo, AltaVista, Inktomi, Google+ Consumer web companies: + web search (crawling, indexing) + advertising + email services + ecommerce + Commodity hardware
  • 18Google 2003 2004
  • 19GFS/HDFS+ Distributed replicated data blocks (64Mb)+ Master-slave architecture (Name Node, Data Nodes)+ Not a general file system+ Access via command line utils and API+ Can’t modify after files written
  • 20 MapReduce + Scalable: + no file IO + no networking + no synchronization + Master-slave architecture+ MapReduce programming model: + Master: divide, schedule, monitor work + functional programming + Slave: actual processing + like UNIX pipeline
  • 21 Data movement+ store and process data on the same nodes+ bring code to data, data “locality”
  • 22Hadoop+ Doug Cutting + Search indexer - Lucene + Web crawler - Nutch + Hadoop + HDFS + MapReduce
  • 23Yahoo!+ 40,000 servers+ 170PB storage+ 1000+ active users+ 5M+ monthly jobs+ email spam filters+ categorization, personalization+ computational advertising
  • Data Base NoSQL 24Revolution+ Needed: + fast read/write time + high concurrency + easy horizontally scalable+ Flat data structure+ Sacrificed: + DB Schema + SQL + Transactions
  • 25NoSQL World+ Key-value: Dynamo, Voldemort, Redis, Riak+ Column (tabular): HBase, Hypertable, Cassandra+ Document store: CouchDB, MongoDB+ Graph: Neo4J, FlockDB+ 120+ products (2012)
  • 26Hadoop stack
  • 27Hadoop tools+ Pig + high level scripting language (PigLatin) + converts to MapReduce jobs+ Hive + SQL like queries on dat in HDFS + converts in MapReduce jobs
  • 28Hadoop data movement
  • 29Typical hadoop usage + Text mining + Pattern recognition + Recommendation systems (collaborative filtering) + Prediction models + Risk assessment + Sentiment analysis + Customer churn prediction + Customer segmentation + Point of Sale Transaction analysis + Data “sandbox”
  • 30Application fields+ Science: sensors, genome, weather, satellite, imaging+ Engineering: log analytics, status feeds, network messages, spam filters..+ Product: financial, pharmaceutical, insurance, energy, retail, ecommerce, healthcare, telecom+ Business: analytics, BI
  • 31Business analytics+ Analytic+ Operational Capture, analyze, learn from data
  • 32Who uses Hadoop?
  • 33Why Hadoop?
  • 34Cloudera+ Enterprise support for Apache Hadoop+ Founded 2008, funding $141 M+ Employee 230+ Products: + CDH 4 (cloudera distrobution hadoop) + Impala + Consulting and training
  • 35MapR+ Founded 2009, funding $20M+ MapR Technologies is engineering game- changing Map/Reduce related technologies+ Products: + M3,M5,M7 + NFS, no single node failure + NOT open source !
  • 36HortonWorks+ Founded 2011+ Yahoo spin-off+ Products: + HDP distribution + tools
  • 37Hadoop Ecosystem
  • 38Big Data Landscape
  • 39Splunk+ Founded 2003, raised $230M, IPO 2011, Market cap $3.35B+ Machine logs analysis, operational intelligence+ Collecting, searching, monitoring
  • 40Datameer+ Founded 2009, Funding $17,8M+ Big data: + Data integration + Data Analytics + Data Visualization
  • 41Datasift+ Founded 2010, funding $29.7M+ Data platform for social web+ Aggregate and filter data
  • 42Infochimps+ Founded 2009, funding $5.5M+ Transitioned from data marketpalce to big data platform+ End-to-end big data solution, real time
  • 43Tableau software+ Founded 2003, funding $15M+ Big data analytics+ Big data visualization
  • Big data Startups 44 2012+ Platfora, in memory BI on Hadoop+ Sumologic, log file analysis+ Hadapt, Hadoop+RDBSM+ Metamarkets, patterns in data flow+ DataStax, consulting, training+ Karmasphere, BI, analytics on Hadoop
  • Big data startups 45 2013!+ 10gen, MongoDB+ ClearStory, big data aggregation + analytics+ Continuuity, Hadoop API+ Parstream, database analytics+ Zoomdata, data visualization+ Climate corporation, predictive analytics
  • 46Big data by industry
  • 47Big data Processing Batch interactive stream processing minutes to Millisecond to Query time continues hours seconds data volume TB to PT GB to PB continuesprogramming MapReduce Queries DAG model Users Developers Analysts Developers HadoopOpen Source Drill, Impala Storm, Kafka mapreduce
  • 48New technologies+ Real time quering + Drill (based on Google Dremmel) + Impala (Cloudera)+ Data stream processing + Storm (Twitter), real time analytics + Kafka (LinkedIn), messaging system
  • 49Machine learning + Predictive analytics + Patterns discovery + Data mining + Tools: + Mahout + R
  • 50Big data revolution+ Google: GFS, MapReduce, BigTable,+ Yahoo: Hadoop+ Amazon: DynamoDB+ Facebook: Cassandra, HBase+ Twitter: FlockDB, Storm+ LinkedIn: Vondelmort, Kafka
  • 51Observations+ Game changing technologies come from big companies+ Open Source (!)+ Start-up ecosystem+ Less general, more specialized+ Next step: big data analytics and visualization
  • 52Data scientist+ Machine Learning+ Data Mining+ Statistics+ Software Engineering+ Hadoop/MapReduce/HBase/Hive/Pig+ Java, Python, C/C+, SQL“By 2018, the United States alone could face a shortage of 140,000 to 190,000people with deep analytical skills as well as 1.5 million managers and analysts withthe know-how to use the analysis of big data to make effective decisions.”
  • Big Data Products 53MindMap
  • 54Contacts+ Leonid Zhukov, Ph.D.+ School of Applied Mathematics and Information Science Higher School of Economics, NRU-HSE+