Cloudera 助力台灣大數據產業的發展

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講師:Cloudera 副總裁 苗凱翔博士

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  • Hi,where is the k3 and track B-2?
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Cloudera 助力台灣大數據產業的發展

  1. 1. 1  ©  Cloudera,  Inc.  All  rights  reserved.   Cloudera  助力台湾   大数据产业的发展   Kai  X.  Miao  (苗凯翔)   Vice  President,  Cloudera  Corpora@on  
  2. 2. 2  ©  Cloudera,  Inc.  All  rights  reserved.   Big  Data  Is  Only  GeGng  Bigger   Par@cularly  Relevant  in  the  Telecom  Space   Data  Growth   STRUCTURED  DATA  –  10%   COMPLEX  DATA  –  90%   1980   TODAY   USER  PROFILES       USAGE  DATA     MOBILE  &  DEVICES   NETWORK   MARKETING  &  CRM   PUBLIC  &  TRADE   3rd Platform Clients Rich User Experiences IOT Clients By 2020,world data will reach 40ZB In 2012,we have 2.8ZB1
  3. 3. 3  ©  Cloudera,  Inc.  All  rights  reserved.   TradiGonal  Data  Architecture  Can’t  Handle  Big  Data     Instrumenta@on   Storage  Grid  (Original  Raw  Data)   Collec@on   ETL  Compute  Grid   BI  Reports  +  Interac@ve  Apps   RDBMS/EDW   Can’t  explore  original  raw  data   Can’t  scale     Sending  data  to  graveyard  
  4. 4. 4  ©  Cloudera,  Inc.  All  rights  reserved.   A  Major  LimitaGon  of  RDBMS/EDW     •  Schema  must  be  created  before  any  data  can  be  loaded   •  An  explicit  load  opera@on  has  to  take  place  which  transforms   data  to  DB  internal  structure     •  New  columns  must  be  added  explicitly  before  new  data  for   such  columns  can  be  added  into  the  data  base     Schema-­‐on-­‐Write    
  5. 5. 5  ©  Cloudera,  Inc.  All  rights  reserved.   Expanding  Data  Requires  A  New  Approach   ©2014  Cloudera,  Inc.  All  rights  5   1980s   Bring  Data  to  Compute   Now   Bring  Compute  to  Data   RelaGve  size  &  complexity   Data   InformaGon-­‐centric   businesses  use  all  data:       Mul@-­‐structured,     internal  &  external  data     of  all  types   Compute   Compute   Compute   Process-­‐centric     businesses  use:     • Structured  data  mainly   • Internal  data  only   • “Important”  data  only       Comput e   Comput e   Comput e   Data   Data   Data   Data  
  6. 6. 6  ©  Cloudera,  Inc.  All  rights  reserved.   Hadoop改变处理数据方式 Hadoop方式  传统方式 $30,000+  per  TB   •  Hard  to  scale   •  Network  is  a  bogleneck   •  Only  handles  rela@onal  data   •  Difficult  to  add  new  fields  &  data  types   昂贵的、专有的、“可靠的”服务器 昂贵的软件许可   Network   数据存储   (SAN,  NAS)   计算   (RDBMS,  EDW)   $300  -­‐  $1,000  per  TB   •  Scales  out  forever   •  No  boglenecks   •  Easy  to  ingest  any  data   •  Agile  data  access   廉价的PC服务器 便宜的、开源的软件   Compute   (CPU)   Memory   Storage   (Disk)   z   z  
  7. 7. 7  ©  Cloudera,  Inc.  All  rights  reserved.  7   A  Strong  Track  Record  of  Innova@on   2008   CLOUDERA  FOUNDED   BY  MIKE  OLSON   AMR  AWADALLAH  &   JEFF  HAMMERBACHER   2009   HADOOP  CREATOR   DOUG  CUTTING   JOINS  CLOUDERA   2009   CLOUDERA  RELEASES  CDH   THE  FIRST  COMMERCIAL     APACHE  HADOOP   DISTRIBUTION   2010   CLOUDERA  MANAGER:   FIRST  MANAGEMENT   APPLICATION  FOR   HADOOP   2011   CLOUDERA  REACHES   100  PRODUCTION   CUSTOMERS   2011   CLOUDERA   UNIVERSITY  EXPANDS   TO  140  COUNTRIES   2012   CLOUDERA  ENTERPRISE  4   THE  STANDARD  FOR   HADOOP  IN  THE   ENTERPRISE   2012   CLOUDERA   CONNECT  REACHES   300  PARTNERS   2014   THE  ENTERPRISE   DATA  HUB   LAUNCHED   2013   CLOUDERA  IMPALA   CLOUDERA  NAVIGATOR   CLOUDERA  SEARCH     2013   TOM  REILLY  JOINS  AS  CEO   OVER  800  PARTNERS     IN  CLOUDERA  CONNECT   2014   SERIES  F  FUNDING  WITH   INTEL  AS  KEY  PARTNER   OVER  900  PARTNERS     IN  CLOUDERA  CONNECT   2014   CLOUDERA   ENTERPRISE  5   CDH Cloudera Manager CLOUDERA   ENTERPRISE   4   ASK  BIGGER   QUESTIONS   ENTERPRISE   DATA  HUB   CLOUDERA   ENTERPRISE   5  
  8. 8. 8  ©  Cloudera,  Inc.  All  rights  reserved.   Cloudera公司简介 ©2014  Cloudera,  Inc.  All  rights  reserved.   创始 2008年, 由前 员工共同创始 員工人數 900人以上 世界级技術支持 24x7的全球工作人员 积极主动与预测技術支持方案 关键任务 数以千计的企业用户 几百多个付费客户 最广泛的生态系统 1400多个商业合作伙伴 Cloudera University 培训100,000人以上 开源领袖 Cloudera的员工是业界领先的开发者和提供商 我们与英特尔的合作将能成功地开拓市场
  9. 9. 9  ©  Cloudera,  Inc.  All  rights  reserved.  9   Open  Source   Scalable   Flexible   Cost-­‐EffecGve   ✔   Managed   ✖   Open   Architecture   ✖   Secure  and   Governed   ✖   ✔   ✔   ✔   3RD  PARTY   APPS   STORAGE  FOR  ANY  TYPE  OF  DATA   UNIFIED,  ELASTIC,  RESILIENT,  SECURE             CLOUDERA’S  ENTERPRISE  DATA  HUB   BATCH   PROCESSING   MAPREDUCE   ANALYTIC   SQL   IMPALA   SEARCH   ENGINE   SOLR   MACHINE   LEARNING   SPARK   STREAM   PROCESSING   SPARK  STREAMING   WORKLOAD  MANAGEMENT  YARN   FILESYSTEM   HDFS   ONLINE  NOSQL   HBASE   DATA   MANAGEMENT   CLOUDERA  NAVIGATOR   SYSTEM   MANAGEMENT   CLOUDERA  MANAGER   SENTRY   DBMS   Sensors   LOGS   Sqoop   Flume  
  10. 10. 10  ©  Cloudera,  Inc.  All  rights  reserved.   WEB/MOBILE  APPLICATION   ENTERPRISE  DATA   WAREHOUSE     ENTERPRISE   REPORTING  BI  /  ANALYTICS  DATA   MODELING   DEVELOPER   SDKs   CLOUDERA   MANAGER   CLOUDERA   NAVIGATOR   ENTERPRISE  DATA  HUB   Security  Admins   System  Admins   Engineers   Data  Scien@sts   Analysts   Business  Users   Customers  &  End  Users   SYS  LOGS   WEB  LOGS   FILES   RDBMS   The  Modern  InformaGon  Architecture  
  11. 11. 11  ©  Cloudera,  Inc.  All  rights  reserved.   Customer  Success  Across  Industries   Financial     &  Business   Services   Telecom   Technology   Healthcare   Life  Sciences   Media   Retail   Consumer   Energy   Public  Sector  
  12. 12. 12  ©  Cloudera,  Inc.  All  rights  reserved.       客户360度分析   • Enhanced  customer   experience  &  support   • Personaliza@on,  targeted   offerings,  loyalty  programs   • Sen@ment  analysis   渠道优化   • Campaign   management   • Selec@on  process   op@miza@on   供应链优化   • Manufacturing  process   efficiency   • Supplier/merchant   management   ⻛风险管理   • Fraud  detec@on   • Intrusion  detec@on  &   digital  forensics   审计   • Regulatory  compliance   (reten@on,  privacy)   • Usage  analysis  and   media@on   • e-­‐Discovery   市场资讯   • Compe@@ve  analysis   • Economic  factor   analysis   • Customer   segmenta@on   数据服务   • Data  as-­‐a-­‐product   • Data  enriched  with   insights/inferences   Cloudera⼤大数据应⽤用案例种类   12
  13. 13. 13  ©  Cloudera,  Inc.  All  rights  reserved.   制造业的数据来自哪里? 设备&传感器 •  Device  Readings   •  Device  Performance   •  Device  Diagnos@cs   •  Bagery  /  Power   Consump@on   •  Sotware  Logs   •  Environmental   Interac@ons   •  R&D   •  Quality  /  Tes@ng   工厂&作业 •  MES   •  Sensors   •  Video  /  Surveillance   •  Line  Produc@vity   •  Machines   •  Staffing  /  Scheduling   供应链&库存 •  ERP   •  Supplier  /  Manufacturer   •  Orders  /  Receivables   •  Commodity  Supplies  /   Prices   市场 & CRM •  Transac@ons   •  Accounts     •  Warran@es  /   Atermarket   •  Customer  Service  Logs   •  Campaigns  /   Promo@ons   •  Website  /  SEO   •  Affiliates  /  Merchants   •  Surveys   •  Compe@@ve   Intelligence   公共 & 交易 •  Market  Intelligence   •  Policy  /  Regula@on   •  Demographic  /  Census   •  Psychographic   •  Infla@on  /  Macroeconomic   •  Gas  Prices   •  Labor  Sta@s@cs   •  Social  /  Search   •  Public  Health  Data   •  Clinical  Studies   •  Store  Schema@cs   •  Journals  /  Editorial   •  Seismic  /  Specula@on  
  14. 14. 14  ©  Cloudera,  Inc.  All  rights  reserved.   •  reduce  the  cost  of  sending   deepwater  drillships  out   into  the  ocean  (1M$/day)   •  doing  a  beger  job  of   processing  the  vast   amounts  of  data  that  can   help  iden@fy  reservoirs  of   oil(0.5PB)   •  Chevron  gathers  informa@on  in   five  dimensions  –  the  x  and  y   coordinates  of  both  the  wave’s   source  and  target,  along  with  the   @me  it  was  collected.     •  Construct  picture  of  what  the   terrain  looks  like  under  the  ocean   floor   •  The  company  uses  CDH  to  sort   that  data.   Solu@on   优化运营–雪佛龙   •  The  more  data  Chevron  can   collect,  the  beger  it  can  find   pockets  of  oil  and  natural   gas  underground.     •   Hadoop  can  do  some  of  the   seismic  data  processing  in  a   less  expensive  way  –  10x  less   than  tradi@onal  technologies   on  average.     Challenge   Benefit   Chevron  is  reducing  their  cost  of  sending  deepwater  drillships  into   the  ocean  by  more  precisely  iden@fying  oil  reservoirs.    
  15. 15. 15  ©  Cloudera,  Inc.  All  rights  reserved.   Automo@ve   &  Industrial   Problem   Solu+on   Backgroun d   Proac+ve  Quality  Assurance   Build  machine  learning  algorithms  that  iden@fy  produc@on  anomalies  prior  to  field  tes@ng   and  find  performance  flaws  that  could  not  be  iden@fied  in  R&D.   Silos  Limit  Op+ons   Legacy  systems  hold  historical  data  from  produc@on  line  telemetry,  factory  surveillance  and   sensors,  call  centers,  in-­‐car  telema@cs,  etc.  That  data  is  useless  if  it  is  kept  offline  and  in  silos.   Anomaly  Detec+on   Spark  includes  MLLib,  a  library  of  machine  learning  algorithms  for  large  data,   enabling  clustering  to  iden@fy  outliers  from  typical  produc@on  pagerns.   Use  Case   卡特彼勒   卡特彼勒公司总部位于美国伊利诺州。是世界上最 大的工程机械和矿山设备生产厂家、燃气发动机和 工业用燃气轮机生产厂家之一,也是世界上最大的 柴油机厂家之一。  
  16. 16. 16  ©  Cloudera,  Inc.  All  rights  reserved.   Telco  Consumer  Profile   16   ©2014  Cloudera,  Inc.  All  rights   Contact,  Credit  info,  date   of  renewal   Device  type:  phone,  mobile   broadband,  tablet   Data/Voice  Usage  and  Top-­‐ up   App  Preference,  interests,   usage   Usage  trends:  @me  of  day,   data  amounts   Loca@on   Website  usage   Social  Networks   Like/dislike,  profile  info  
  17. 17. 17  ©  Cloudera,  Inc.  All  rights  reserved.   ©2014  Cloudera,  Inc.  All  rights  reserved.   Use  Case   Problem   Solu+on   Partners   Ac(onable  Sen(ment  Analysis   Isolate  customer  profiles  to  personalize  mix  of  plans,  services,  offers  based  on   convergence  of  informa@on  from  network,  GPS,  social,  call  centers,  accounts,  etc.   Can’t  Scale  Beyond  Silos   Current  systems  can  not  integrate  social,  telemetric,  and  systems  data  in  real   @me  with  historical  data  to  tailor  product  mix  and  incen@ve  plans  to  the  user.   Calculate  Anything   HBase  is  a  real-­‐@me  database  accommoda@ng  complex  historic  data.  Spark  and   Impala  converge  ETL,  analy@cs,  and  repor@ng  for  on-­‐demand  modeling.   Customer   360o  View   17  
  18. 18. 18  ©  Cloudera,  Inc.  All  rights  reserved.   Where  Is  the  Financial  Services  Data?   Mapping  and  Consolida@on  Are  the  Tip  of  the  Iceberg  for  Big  Data   Retail  Banking   •  Bank  Transac@ons   •  Customer  Data   •  ATM  Ac@vity   •  Online  Ac@vity   •  Mobile  Ac@vity   •  Demographic  /  Census   Data   •  Marke@ng  /  CRM   •  Social  /  Sen@ment   Credit  Cards  &   Payments   •  Card  Transac@ons   •  Customer  Data   •  Online  Ac@vity   •  Demographic  /  Census   Data   •  Marke@ng  /  CRM   •  Integra@on  with   Retailers  /  Loyalty   •  Social  /  Sen@ment     Investment   Banking   •  Trade  Data   •  Customer  Data   •  Web  Logs   •  Research  /  Publica@ons   •  Market  Data   •  Communica@ons  /   Documenta@on   Insurance   •  Claims  /  Policy  Data   •  Customer  Data   •  Demographic  /  Census   Data   •  Weather  Data   •  Vehicle  Telemetry   •  Video  /  Surveillance   •  Sensors   •  Internet  of  Things   Services  &  SROs   •  Trade  Data   •  Communica@ons  /   Documenta@on   •  Market  Data   •  Research  /  Publica@ons   •  Surveys  
  19. 19. 19  ©  Cloudera,  Inc.  All  rights  reserved.   Data  silos  spread  across   company  with  80+  years’   history   •  Analysis  on  1  state  takes  24   hours   •  Can’t  analyze  all  50  states  at   once   Universal  data  archive  on   Cloudera   •  Supports  storage,  ETL,   applied  math   Solu@on   Customer  Spotlight:  Allstate   Holis@c  analysis  on  all  50   states  in  16  hours   •  75X  faster  performance   Challenge   Benefit   Combining  80+  years  of  data  across  all  business  units   &  all  50  states.  
  20. 20. 20  ©  Cloudera,  Inc.  All  rights  reserved.   Thank  you!  

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