Your SlideShare is downloading. ×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Continuous Intelligence: Staying Ahead with Streaming Analytics

335
views

Published on

The Briefing Room with Mark Madsen and SQLstream …

The Briefing Room with Mark Madsen and SQLstream
Live Webcast Mar. 12, 2013

The battle cry of “time to insight” continues to change the way organizations seek insights from their data, big and small. One increasingly popular strategy focus on analyzing data streams, ranging from social media to machine-generated data captured in logs. By focusing on the kind of continuous intelligence that can flow from such analysis, organizations can stay ahead of their competitors by seizing new opportunities, and often avoiding problematic disruptions.

Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature, as he explains how a new wave of technologies offers a compelling alternative to the traditional means for generating insights. He’ll be briefed by Damian Black of SQLStream who will explain how his company’s platform was designed to enable real-time analysis of multiple data streams. He’ll discuss how streaming analytics can remove the gap between traditional business intelligence and operational systems.

Visit: http://www.insideanalysis.com

Published in: Technology

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
335
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. The Briefing Room
  • 2. Welcome Host: Eric Kavanagh eric.kavanagh@bloorgroup.comTwitter Tag: #briefr The Briefing Room
  • 3. Mission !   Reveal the essential characteristics of enterprise software, good and bad !   Provide a forum for detailed analysis of today s innovative technologies !   Give vendors a chance to explain their product to savvy analysts !   Allow audience members to pose serious questions... and get answers!Twitter Tag: #briefr The Briefing Room
  • 4. MARCH: Operational Intelligence April: INTELLIGENCE May: INTEGRATION June: DATABASETwitter Tag: #briefr The Briefing Room
  • 5. Operational Intelligence REAL-TIME…Processing Monitoring Alerts/triggers/actionsTwitter Tag: #briefr The Briefing Room
  • 6. Analyst: Mark Madsen  Mark Madsen is president of Third Nature, Inc.Twitter Tag: #briefr The Briefing Room
  • 7. SQLstream ! SQLstream is an enterprise software company focused on making businesses responsive to real-time big data assets !   Its platform provides a relational stream for analyzing large volumes of service, sensor, and machine and log file data !   SQL queries in SQLstream generate results continuously as data becomes availableTwitter Tag: #briefr The Briefing Room
  • 8. Damian Black Damian Black is the founder and CEO of SQLstream, a pioneer in Streaming Big Data. Damian has worked for almost two decades in Silicon Valley, with senior roles in a variety of companies including Hewlett-Packard, Neustar, Xacct Technologies and Followap. He has spoken at many conferences, and was on GigaOM’s first Big Data panel in 2008. Damian graduated from Manchester University and was one of the first research scientists to join HPLabs Europe. He was selected for the International Management Challenge in conjunction with the Financial Times and Ashridge business school while at Hewlett-Packard. Damian is the author of eleven granted patents with five more pending.Twitter Tag: #briefr The Briefing Room
  • 9. BIG DATA ON TAP™ Continuous Intelligence:Staying Ahead with Streaming Log File Analytics M a r c h 2 0 1 3 D a m i a n B l a c k , C E O , S Q L s t r e a m Copyright © SQLstream Inc.
  • 10. M a c h i n e - G e n e ra t e d B i g D a t a E x p l o s i o nHigh volume, high velocity, structured and unstructured data from software platforms, applications and systems Machine-generated data will increase to 42% of all data by 2020, up from GPS 11% in 2005. Telematics “The Digital Universe in 2020” IDC IP Networks, Video Servers, Social Media, Security Servers, Applications, Storage Networks Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 10
  • 11. O P E R AT I O N A L I N T E L L I G E N C E Bridging the Chasm Between Analytics and Operations VELOCITY UNSTRUCTURED DATA VOLUME VARIETY VISUAL VALUE STRUCTURED DATA Operational IntelligenceTRANSACTIONS ➔  Predictive analytics ➔  Automated actions Business Intelligence ➔  Ops optimization ➔  Post-hoc analysis ➔  Tactical execution Business Applications ➔  Data warehousing ➔  Transactions ➔  Strategic direction ➔  Everyday business Real-time, continuous Historical, periodic Real-time, continuous Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 11
  • 12. O P E R AT I O N A L I N T E L L I G E N C E Bridging the Chasm Between Analytics and Operations VELOCITY UNSTRUCTURED DATA VOLUME VARIETY VISUAL VALUE STRUCTURED DATA Operational IntelligenceTRANSACTIONS ➔  Predictive analytics ➔  Automated actions Business Intelligence ➔  Ops optimization ➔  Post-hoc analysis ➔  Tactical execution Business Applications ➔  Data warehousing ➔  Transactions ➔  Strategic direction ➔  Everyday business Real-time, continuous Historical, periodic Real-time, continuous Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 12
  • 13. M AC H I N E D AT A T O O P E R AT I O N A L I N T E L L I G E N C EPROACTIVEREACTIVE Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 13
  • 14. M AC H I N E D AT A T O O P E R AT I O N A L I N T E L L I G E N C EPROACTIVEREACTIVE Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 14
  • 15. REAL-TIME WEB SERVER LOG MONITORING M o z i l l a ( G o o g l e : “ Yo u t u b e M o z i l l a G l o w ” )Real-time monitoring across all download web Web Server Log Files (Remote) servers across the world simultaneously. Streaming collection, real-time analysis and continuous integrationCollect by location Remote agents transform log files into real-time streams Hadoop HBase Analyze Real-time analysis & aggregation by location Share Continuous ETL into Hadoop Hbase Internet ‘Glow’ app for real-time visualization Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 15
  • 16. REAL-TIME WEB SERVER LOG MONITORINGM o z i l l a ( G o o g l e : “ Yo u t u b e M o z i l l a G l o w ” ) parse parse parse parse off Filter Merge parse parse Logs Parse Bad recs Streaming Analyze Filter out Add Analytics Errors Bots Location Streaming Visualization HBase Historical Charts Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 16
  • 17. REAL-TIME WEB SERVER LOG MONITORING M o z i l l a ( G o o g l e : “ Yo u t u b e M o z i l l a G l o w ” )Mozilla Firefox 4 – Real-time Download MonitorContinuous processing of download requestsReal-time integration with Hadoop and HBase Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 17
  • 18. M AC H I N E D AT A Where is the intelligence?Transaction TRANS,2013-02-17-15:30:22,3458783,2347897953,128.56.0.253,STATUS:-15, DE69975, 4157588342Log DetailsWeb Server [Sun Feb 17 15:30:49 2013] [notice] srv-sfo-08 caught SIGTERM, shutting downLogs [Sun Feb 17 15:30:49 2013] [notice] Apache/2.2.21 -- resuming normal operations TERMINATE,ctl09gsx,01299796304,GMT-08:00,02-17-13,15:21:00,9,387,64ms,02-17-13,15:30:55,0005,CDR Records IP-TO-IP,4157588342,8775715775,1,0,4157588342,RD_AXY_NN0_001,SFR01AAG34,40.50.245.60, 234.234.60.75,65678,411,399,SIP,SANFRANCISCO,0x4B1698,0x0005E,0x49768,4157588342,0198873465 <id>1597831220</id><deviceid>0198873465</deviceid><lat>lat=47.643957</lat><lon>lon= -122.3269</lon><time>2013-02-17T15:37:26Z</time><bearing>223.4535</bearing>Smartphone <id>1597865781</id><deviceid>0198873465</deviceid><lat>lat=47.645982</GPS Updates lat><lon>lon=-122.327500</lon><time>2013-02-17T15:37:26Z</time><bearing>200.6138</bearing> <id>1597940125</id><deviceid>0198873465</deviceid><lat>lat=47.647381</ lat><lon>lon=-122.326501</lon><time>2013-02-17T15:37:26Z</time><bearing>87.4357</bearing> {"created_at:Thu Feb 17 15:30:55 +0000 2013,id:304612775055998976,id_str:Twitter 304612775055998976,text:@MyServiceProvider today sucks, keeps dropped!,source:u006ca href=http:www.url.com rel=nofollow,followers_count:147,friends_count:10142, location: San Francisco, time_zone: Pacific, geo_enabled:true, location:u00dcT: -6.1987552,106.8661953, screen_name:APerson Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 18
  • 19. M AC H I N E D AT A Where is the intelligence? TimestampTransaction TRANS,2013-02-17-15:30:22,3458783,2347897953,128.56.0.253,STATUS:-15, DE69975, 4157588342Log Details TimestampWeb Server [Sun Feb 17 15:30:49 2013] [notice] srv-sfo-08 caught SIGTERM, shutting downLogs [Sun Feb 17 15:30:49 2013] [notice] Apache/2.2.21 -- resuming normal operations Timestamp TERMINATE,ctl09gsx,01299796304,GMT-08:00,02-17-13,15:21:00,9,387,64ms,02-17-13,15:30:55,0005,CDR Records IP-TO-IP,4157588342,8775715775,1,0,4157588342,RD_AXY_NN0_001,SFR01AAG34,40.50.245.60, 234.234.60.75,65678,411,399,SIP,SANFRANCISCO,0x4B1698,0x0005E,0x49768,4157588342,0198873465 <id>1597831220</id><deviceid>0198873465</deviceid><lat>lat=47.643957</lat><lon>lon= -122.3269</lon><time>2013-02-17T15:37:26Z</time><bearing>223.4535</bearing>Smartphone <id>1597865781</id><deviceid>0198873465</deviceid><lat>lat=47.645982</GPS Updates lat><lon>lon=-122.327500</lon><time>2013-02-17T15:37:26Z</time><bearing>200.6138</bearing> Timestamp <id>1597940125</id><deviceid>0198873465</deviceid><lat>lat=47.647381</ lat><lon>lon=-122.326501</lon><time>2013-02-17T15:37:26Z</time><bearing>87.4357</bearing> Timestamp {"created_at:Thu Feb 17 15:30:55 +0000 2013,id:304612775055998976,id_str:Twitter 304612775055998976,text:@MyServiceProvider today sucks, keeps dropped!,source:u006ca href=http:www.url.com rel=nofollow,followers_count:147,friends_count:10142, location: San Francisco, time_zone: Pacific, geo_enabled:true, location:u00dcT: -6.1987552,106.8661953, screen_name:APerson Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 19
  • 20. M AC H I N E D AT A Where is the intelligence? Timestamp Customer Fail Code Mobile #Transaction TRANS,2013-02-17-15:30:22,3458783,2347897953,128.56.0.253,STATUS:-15, DE69975, 4157588342Log Details Timestamp ServerWeb Server [Sun Feb 17 15:30:49 2013] [notice] srv-sfo-08 caught SIGTERM, shutting downLogs [Sun Feb 17 15:30:49 2013] [notice] Apache/2.2.21 -- resuming normal operations Timestamp Mobile # TERMINATE,ctl09gsx,01299796304,GMT-08:00,02-17-13,15:21:00,9,387,64ms,02-17-13,15:30:55,0005,CDR Records Term Reason Device ID IP-TO-IP,4157588342,8775715775,1,0,4157588342,RD_AXY_NN0_001,SFR01AAG34,40.50.245.60, 234.234.60.75,65678,411,399,SIP,SANFRANCISCO,0x4B1698,0x0005E,0x49768,4157588342,0198873465 Device ID Location <id>1597831220</id><deviceid>0198873465</deviceid><lat>lat=47.643957</lat><lon>lon= -122.3269</lon><time>2013-02-17T15:37:26Z</time><bearing>223.4535</bearing>Smartphone <id>1597865781</id><deviceid>0198873465</deviceid><lat>lat=47.645982</GPS Updates lat><lon>lon=-122.327500</lon><time>2013-02-17T15:37:26Z</time><bearing>200.6138</bearing> Timestamp <id>1597940125</id><deviceid>0198873465</deviceid><lat>lat=47.647381</ lat><lon>lon=-122.326501</lon><time>2013-02-17T15:37:26Z</time><bearing>87.4357</bearing> Timestamp {"created_at:Thu Feb 17 15:30:55 +0000 2013,id:304612775055998976,id_str:Twitter 304612775055998976,text:@MyServiceProvider today sucks, keeps dropped!,source:u006ca href=http:www.url.com rel=nofollow,followers_count:147,friends_count:10142, location: San Francisco, Service Provider time_zone: Pacific, geo_enabled:true, location:u00dcT: -6.1987552,106.8661953, screen_name:APerson Location Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 20
  • 21. O P E R AT I O N A L S T R E A M I N G B I G D AT A – PA I N P O I N T SDATA EXPLOSION Too costly to analyse voluminous real-time data BUSINESS AGILITY Too slow to respond to new requirements COMPLEXITY Too difficult to build & maintain real-time apps Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 21
  • 22. O P E R AT I O N A L S T R E A M I N G B I G D AT A – PA I N P O I N T SDATA EXPLOSION Too costly to analyse voluminous real-time data SQLstream slashes TCO for real-time analysis. BUSINESS AGILITY Too slow to respond to new requirements SQLstream allows you to add new apps easily. COMPLEXITY Too difficult to build & maintain real-time apps SQLstream eliminates your development risk. Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 22
  • 23. C O N T I N U O U S O P E R AT I O N A L I N T E L L I G E N C E M2M Automotive Telecom Banking LogsManufacturing Oil & Gas RFIDs GPS Logistics Servers Smart grid Networks Retail Real-time alerts, action Sensors Social and media visualization Telematics Data centers Internet Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 23
  • 24. C O N T I N U O U S O P E R AT I O N A L I N T E L L I G E N C E •  Collect, transform and deliver: ETL++ M2M •  Analyze unstructured data & enhance •  Predictive analytics & actions Automotive Telecom Banking LogsManufacturing Oil & Gas RFIDs GPS Logistics Servers Smart grid Networks Retail Real-time alerts, action Sensors Enhance Store and Social media with detail and visualization Telematics aggregate historical Data information data centers Internet Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 24
  • 25. M O V I N G F R O M H I G H L AT E N C YTO REAL-TIME RESPONSIVENESS Traditional approach leads to high latency COLLECT CLEANSE ENRICH ANALYZE SHARE HIGH LATENCY Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 25
  • 26. M O V I N G F R O M H I G H L AT E N C YTO REAL-TIME RESPONSIVENESS Traditional approach leads to high latency COLLECT SQLstream streaming approach: »  Continuous Parallel Dataflow Execution CLEANSE »  Generate real-time answers immediately »  Deliver and share the results immediately ENRICH ANALYZE SHARE LOW LATENCY Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 26
  • 27. S Q L S T R E A M D AT A F L O W T E C H N O L O G YP I P E L I N I N G A N D S U P E R S C A L A R PA R A L L E L P R O C E S S I N G Query = Processor Fine-grained parallelism: simple, massively scalable, super fast. Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 27
  • 28. S H A R E S T R E A M I N G B I G D AT AUse SQLstream and ISO/ANSI standard SQL »  Proven performance, optimization and scalability »  Rapid app development with familiar language »  Leverage existing SQL skills & investment Streaming SQL Views CREATE VIEW compliant_orders AS SELECT STREAM * FROM orders OVER sla GENERATES THE STREAM JOIN shipments ON orders.id = shipments.orderid OF NEW YORK ORDERS WHERE city = New York SHIPPING WITHIN A WINDOW sla AS SERVICE LEVEL OF 1hr (RANGE INTERVAL 1 HOUR PRECEDING) Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 28
  • 29. A S T R E A M I N G S Q L Q U E RY CLOUD INFRASTRUCTURE MONITORING WITH BOLLINGER BANDSSELECT STREAM ROWTIME, url, numErrorsLastMinute FROM ( SELECT STREAM ROWTIME, url, numErrorsLastMinute, AVG(numErrorsLastMinute) OVER lastMinute AS avgErrorsPerMinute, STDDEV(numErrorsLastMinute) OVER lastMinute AS stdDevErrorsPerMinute FROM ServiceRequestsPerMinute WINDOW lastMinute AS (PARTITION BY url RANGE INTERVAL ‘1’ MINUTE PRECEDING) ) AS S WHERE S.numErrorsLastMinute > S.avgErrorsPerMinute + 2 * S.stdDevErrorsPerMinute; BUSINESS NEED: Detect run-away applications before resource consumption becomes an issue. Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 29
  • 30. T H E R E A L - T I M E DATA M A N AG E M E N T H E A DAC H E Finance Supply Chain CRM Operations Business Intelligence: & & & & Hadoop HBase & Accounting ERP Billing Management Data Warehouses TIME, MONEY, COMPLEXITY Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 30
  • 31. T H E R E A L - T I M E DATA M A N AG E M E N T S O LU T I O N Finance Supply Chain CRM Operations Business Intelligence: & & & & Hadoop HBase & Accounting ERP Billing Management Data Warehouses STREAMING STEAMING EVENT STREAMING CONTINUOUS ANALYTICS AND CORRELATION ALERTS & ALARMS ETL AGGREGATION Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 31
  • 32. S Q L S T R E A M S T A N D A R D I N T E G R AT I O N A D A P T E R S Core Database Adapter DATA B A S E S Table Reader Table Update Table Lookup (any JDBC) B I G D A T A Hadoop BigQuery + HDFS + HBase MACHINE DATA Log Files XML Parse Sockets JDBC + Remote Agent + XPath + TCP + JMS XML + FileWriter + UDP + log4j + FileReader Middleware Web Feeds GATE Email + Twitter + RSS + ATOM etc T Semantic Streaming STORM Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 32
  • 33. S T R E A M I N G V I S U A L I Z AT I O N Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 33
  • 34. R E A L - T I M E O P E R AT I O N A L I N T E L L I G E N C E M A R K E T C O M PA R I S O NENTERPRISE OPERATIONAL INTELLIGENCE OPERATIONAL INTELLIGENCE REQUIREMENT WITH OTHERS WITH SQLSTREAM Time Series Analytics Simplistic answers without time series. Comprehensive times series support. Complex Analysis Simple pattern matching and statistics. Elegantly solves hardest problems. Join Correlate Does not combine or join streams. Joins data streams in real-time. Enrich Integrate Does not enrich or integrate data. Gives rich answers in real-time. Big Data Scalability No parallel processing; limited scalability. Massively parallel, auto-optimizing. Painless TCO Very expensive, proprietary, with only Low TCO, ANSI/ISO standard basic visualization. queries, rich real-time visualization. Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 34
  • 35. S Q L S T R E A M : B I G DATA O N TA P ™ , d e l i ve r e d Slashing TCO for real-time analysis DATA EXPLOSION •  Scales easily without transaction bottlenecks. Adding new apps easily BUSINESS AGILITY •  Shares dynamic results and data across the organization. Eliminating the development risk COMPLEXITY •  Fine-grained parallel processing: simple, scalable and fast. Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 35
  • 36. O P E R AT I O N A L I N T E L L I G E N G E - B E YO N D I T ENVIRONMENTAL TRANSPORTATION NETWORKSEnvironmental Monitoring Location-based services Machine-to-MachineSmart Grid Cars as Sensors Logistics Copyright © 2013 | +1 877 571 5775 | inquiries@sqlstream.com | 36
  • 37. QUESTIONS Copyright © SQLstream Inc.
  • 38. About  the  Presenter   Mark  Madsen  is  president  of  Third   Nature,  a  technology  research  and   consul8ng  firm  focused  on  business   intelligence,  data  integra8on  and  data   management.  Mark  is  an  award-­‐winning   author,  architect  and  CTO  whose  work   has  been  featured  in  numerous  industry   publica8ons.  Over  the  past  ten  years   Mark  received  awards  for  his  work  from   the  American  Produc8vity    Quality   Center,  TDWI,  and  the  Smithsonian   Ins8tute.  He  is  an  interna8onal  speaker,   a  contributor  at  Forbes  Online  and   Informa8on  Management.  For  more   informa8on  or  to  contact  Mark,  follow   @markmadsen  on  TwiMer  or  visit     hMp://ThirdNature.net    
  • 39.  Con.nuous  Intelligence:  Staying  Ahead  with  Streaming  Analy.cs        March,  12  2013    Mark  Madsen  www.ThirdNature.net  @markmadsen  
  • 40. The “E” in EDWwas a lie…
  • 41. Transac.ons  vs.  Events  Transac8ons:   ▪  Each  one  is  valuable   ▪  The  elements  of  a  transac8on  can  be  aggregated  easily   ▪  A  set  of  transac8ons  does  not  usually  have  important  ordering   or  dependency  Events:   ▪  A  single  event  oUen  has  no  value,  e.g.  what  is  the  value  of  one   click  or  one  temperature  reading  in  a  series?   ▪  Some  events  are  extremely  valuable,  but  this  is  only   detectable  within  the  context  of  other  events.   ▪  Elements  of  events  are  oUen  not  easily  aggregated   ▪  A  set  of  events  usually  has  a  natural  order  and  dependencies  
  • 42. General  model  for  organiza.onal  use  of  data  Collect Act on the processnew data Usually days/longer timeframe Analyze AnalyzeMonitor Decide Act Exceptions Causes No problem No idea Do nothing Act within the process Usually real-time to daily
  • 43. You  need  to  be  able  to  support  both  paths   Analytics and BICollectnew data Act on the process Analyze AnalyzeMonitor Decide Act Exceptions Causes Act within the process Streaming technologies
  • 44. Different  Usage  Model  Than  Conven.onal  BI  A)  Monitoring  and  detec8on  is  not  repor8ng  and   dashboards.  Self-­‐service  BI  doesn’t  do  it  B)  Lots  of  data,  decreasing  in  value  as  the  events   recede  in  8me  C)  Analy8cs  oUen  required  to  surface  meaningful   events,  which  requires  collec8on  and  processing   of  (B)  to  process  in  real  8me  to  deliver  (A).  D)  Actua8on:  machine  managed,  human  mediated     The  future  is  not  data  to  eyeballs,  its  machines  to  machines  
  • 45. Measurement  started  with  the  convenient  data   The  convenient  data  is   transac8onal  data.   ▪  Goes  in  the  DW  and  is  used,  even   if  it  isn’t  the  right  measurement.   The  inconvenient  data  is   observa8onal  data.   ▪  It’s  not  neat,  clean,  or  designed   into  most  systems  of  opera8on.   We  need  to  build  infrastructure   that  manages  and  enables  use  of   data  at  rest  and  data  in  mo8on.  
  • 46. Bridge  the  data  warehouse  to  other  uses:  SOA,  not  SQL   New  technologies  are  needed  to  extend  current  capability.   http://flickr.com/photos/higaara/228673603/
  • 47. Ques.ons  1.  Queues  and  streams  process  messages  and   objects.  How  is  that  made  SQL  compa8ble?  2.  Why  SQL  when  the  standard  is  missing   temporal  constructs  for  this?  3.  How  do  you  use  a  single  SQL  statement  across   mul8ple  streams  (i.e.,  scale  out  the  query)?  4.  How  much  work  is  human-­‐monitored,  vs.   human  no8fied,  vs.  machine  actuated?  How   big  is  this  problem,  really?  
  • 48. Ques.ons  5.  What  about  playback?  How  do  you  replay   history  to  trace  an  event?  6.  What  tooling  is  required?  Is  it  possible  to  add   stream  monitoring  and  use  exis8ng  BI  tools,  or   do  we  need  new  end  user  tools?  7.  Linking  the  in-­‐mo8on  to  the  sta8onary,  what   are  the  mechanisms?  
  • 49. About  Third  Nature  Third Nature is a research and consulting firm focused on new andemerging technology and practices in analytics, business intelligence, andperformance management. If your question is related to data, analytics,information strategy and technology infrastructure then you‘re at the rightplace.Our goal is to help companies take advantage of information-drivenmanagement practices and applications. We offer education, consultingand research services to support business and IT organizations as well astechnology vendors.We fill the gap between what the industry analyst firms cover and what ITneeds. We specialize in product and technology analysis, so we look atemerging technologies and markets, evaluating technology and hw it isapplied rather than vendor market positions.
  • 50. Twitter Tag: #briefr The Briefing Room
  • 51. Upcoming TopicsApril: INTELLIGENCEMay: INTEGRATIONJune: DATABASE www.insideanalysis.comTwitter Tag: #briefr The Briefing Room
  • 52. Thank You for Your AttentionCertain images and/or photos in this presentation are the copyrighted property of 123RF Limited, their Contributors or Licensed Partners and are beingused with permission under license. These images and/or photos may not be copied or downloaded without permission from 123RF Limited.Twitter Tag: #briefr The Briefing Room