SlideShare a Scribd company logo
Making Your
Analytics
    Click to edit Master title style
Investment
Pay Off
Bill Eldredge
Sr. Manager, Nokia Data Asset

August 1, 2012
StampedeCon



1
Overview


    •  Nokia and Our Big Data Challenge


    •  Making Hadoop Pay
      •  Cost Avoidance
      •  Product Innovation




2
Nokia’s History:   1865 to Now




3
The Four Cardinal W’s
     Location and Time Add Human Context to Social and Search




    WHAT


    WHO


    WHERE

    WHEN


4
Great Mobile Products That
               Sense the World



            CREATE A LEADING “WHERE” PLATFORM




    WIN IN SMART       CONNECT THE      INVEST IN FUTURE
      DEVICES          NEXT BILLION       DISRUPTIONS



5
Taking Our World Class Map Making
      Capabilities to the Next Level …
                            2.4            1500+
                                                            400+
                                          Validations
                           Million                          Map
                          Changes                        Attributes
                          per Day                   11B+          181
                                                  Probe per
                                                                  Local
                                                    Month
                                                              Field Offices
                         245
                     Field Cars      196
                     Driving the
                       Roads       Countries     Automatic
                                   37k+ km        Feature
                                    Roads       Recognition




    9 out of 10 in-car nav systems use Nokia’s Location
6
                           Platform
One Platform, Enabling
     Contextually Rich Mobile Experiences

Content

Platform     Maps   Positions   Places   Directions   Guidance   Traffic

                                  Smart Data




Apps




 7
Data, Data, Data
         +1B         Search Queries
                     Annually         11B   Probe Points
                                            Processed Monthly




    147M                                           24M
     Map Tiles                                    Route Requests
    Served Daily                                    per Month




+19M       Geocoder
           Requests Daily                   55M        Positioning
                                                       Requests Daily


8
Data Silos




9
Central Big Data Analytics Platform?




"   One cluster for all needs?
"   Do it ourselves, or with help?
"   If we build it, will they come?
"   Will it pay off?
10
Phased Approach
        Oozie
                 Map Reduce         Pig          Hive
       Quartz


                                              HBase
       Scribe                      HDFS
                                              HBase
         FTP
                                              HBase




     2010          2011                      2012
     Build        Enhance,                Make It Robust…
                  Get Data,               and Make It Pay
                Use it Ourselves

11
•  Nokia and Our Big Data Challenge


     •  Making Hadoop Pay
       •  Cost Avoidance
       •  Product Innovation




12
Best of Both Worlds

     •  Key device datasets                          •  Key location datasets
     •  Dedicated reporting team                     •  Reporting largely self-serve
     •  Expensive storage (€20k/TB)                  •  Inexpensive storage (€2k/
                                                        TB)


                          Key Integration Principle
                        Aggregated                     Event-level
                           Data                           Data


                          Projected Savings 2012:
                              €6 Million
13
                                   Nokia Internal Use Only
Integrated Solution
                        Touch-points/Devices

                                                                               Consumer-Facing/
                                      Online Serving                              Front-End
                                                                                (“Fast World”)


                                                                               Enterprise-Facing/
                                                                                   Back-End
                                                                                (“Slow-World”)


                                                                Oracle/MySQL      Reporting
     Activity      Hadoop Cluster
                           Hadoop                                                   Tools
     Logging               Cluster                                               (Cognos, Tableau)
                                                                  Teradata
                                                                                  Advanced
                                                                                  Analysis
                                                                Other RDBMS
                                                                                    Tools
     Streaming
        Data     Bulk Data Sources
      Sources


                 Analytics Platform    Other systems

14
                                      Nokia Internal Use Only
Integrated Solution – Applied to Music
     On-device
     app usage             Touch-points/Devices

                                                                                  Consumer-Facing/
                                         Online Serving                              Front-End
                                                                                   (“Fast World”)


                                                                                  Enterprise-Facing/
                                                                                      Back-End
                                      Improved Music                               (“Slow-World”)
                                     Recommendations
                                                                   Oracle/MySQL      Reporting
     Activity         Hadoop Cluster
                              Hadoop                                                   Tools
     Logging                  Cluster                                               (Cognos, Tableau)
                                                                     Teradata
                                                                                     Advanced
                                                                                     Analysis
                                                                   Other RDBMS
                                                                                       Tools
     Streaming
      Download     CDN    Bulk Data Music
        Data
       logs &      Logs   Sources metadata
      Sources
     Collections
                                                                   Projected Benefit:
                    Analytics Platform    Other systems
                                                                   3M Euros +
15
                                         Nokia Internal Use Only
Overview


     •  Nokia and Our Big Data Challenge


     •  Making Hadoop Pay
       •  Cost Avoidance
       •  Product Innovation




16
The Map Is Changing…

        Cities are
      possibly our                                 60%
         greatest                               World’s Population in
                                                   Cities in 2030
      achievement,
       constantly                RICH
                                                  HUNDREDS
                                                  OF MILLIONS MILLIONS
     evolving as we            Network of
                                Sensors
                                                    of Mobile
                                                    Devices
                                                              of Mobile
                                                                Users
      discover new
      technologies



17
                      Nokia Internal Use Only
…and Needs to Become Personal…

           Synthesis                         How
       A    of Us as Who                               When
          Individuals
     Living                             Interactions
      Map             Where                with the
                                         Real World
                                                       What




18
              Nokia Internal Use Only
…to Everyone




19
               Nokia Internal Use Only
Using Different Types of Data…




     Asserted   Observed                 Reference   Derived
       Data       Data                     Data       Data


     …to Create a Human Motion Graph
20
                      Nokia Internal Use Only

More Related Content

What's hot

Treasure Data: Big Data Analytics on Heroku
Treasure Data: Big Data Analytics on HerokuTreasure Data: Big Data Analytics on Heroku
Treasure Data: Big Data Analytics on Heroku
Salesforce Developers Japan
 
Self-Service Access and Exploration of Big Data
Self-Service Access and Exploration of Big DataSelf-Service Access and Exploration of Big Data
Self-Service Access and Exploration of Big Data
Inside Analysis
 
Hadoop for shanghai dev meetup
Hadoop for shanghai dev meetupHadoop for shanghai dev meetup
Hadoop for shanghai dev meetup
Roby Chen
 
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım ÖrnekleriCDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
didemtopuz
 
Accel Partners New Data Workshop 7-14-10
Accel Partners New Data Workshop 7-14-10Accel Partners New Data Workshop 7-14-10
Accel Partners New Data Workshop 7-14-10
keirdo1
 
Hadoop's Role in the Big Data Architecture, OW2con'12, Paris
Hadoop's Role in the Big Data Architecture, OW2con'12, ParisHadoop's Role in the Big Data Architecture, OW2con'12, Paris
Hadoop's Role in the Big Data Architecture, OW2con'12, Paris
OW2
 
Demonstrating the Future of Data Science
Demonstrating the Future of Data ScienceDemonstrating the Future of Data Science
Demonstrating the Future of Data Science
greenplum
 
Treasure Data and Heroku
Treasure Data and HerokuTreasure Data and Heroku
Treasure Data and Heroku
Treasure Data, Inc.
 
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
Planetek Italia Srl
 
SoftLayer (IAAS) 22 juni 2012
SoftLayer (IAAS)   22 juni 2012SoftLayer (IAAS)   22 juni 2012
SoftLayer (IAAS) 22 juni 2012
Ingram Micro Nederland
 
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
Emulex Corporation
 
2011 04-dsi-javaee-in-the-cloud-andreadis
2011 04-dsi-javaee-in-the-cloud-andreadis2011 04-dsi-javaee-in-the-cloud-andreadis
2011 04-dsi-javaee-in-the-cloud-andreadis
dandre
 
Emergent Distributed Data Storage
Emergent Distributed Data StorageEmergent Distributed Data Storage
Emergent Distributed Data Storage
hybrid cloud
 
APAC Big Data Strategy RadhaKrishna Hiremane
APAC Big Data  Strategy RadhaKrishna  HiremaneAPAC Big Data  Strategy RadhaKrishna  Hiremane
APAC Big Data Strategy RadhaKrishna Hiremane
IntelAPAC
 
Research on big data
Research on big dataResearch on big data
Research on big data
Roby Chen
 
Utilisation du cloud dans les systèmes intelligent
Utilisation du cloud dans les systèmes intelligentUtilisation du cloud dans les systèmes intelligent
Utilisation du cloud dans les systèmes intelligent
Microsoft Technet France
 
Emulex Presents Why I/O is Strategic Global Survey Results
Emulex Presents Why I/O is Strategic Global Survey ResultsEmulex Presents Why I/O is Strategic Global Survey Results
Emulex Presents Why I/O is Strategic Global Survey Results
Emulex Corporation
 
Shams.khawaja
Shams.khawajaShams.khawaja
Shams.khawaja
NASAPMC
 

What's hot (18)

Treasure Data: Big Data Analytics on Heroku
Treasure Data: Big Data Analytics on HerokuTreasure Data: Big Data Analytics on Heroku
Treasure Data: Big Data Analytics on Heroku
 
Self-Service Access and Exploration of Big Data
Self-Service Access and Exploration of Big DataSelf-Service Access and Exploration of Big Data
Self-Service Access and Exploration of Big Data
 
Hadoop for shanghai dev meetup
Hadoop for shanghai dev meetupHadoop for shanghai dev meetup
Hadoop for shanghai dev meetup
 
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım ÖrnekleriCDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
CDRLive & CDRInsight,CDR Verileri ile Đs Zekası ve Kullanım Örnekleri
 
Accel Partners New Data Workshop 7-14-10
Accel Partners New Data Workshop 7-14-10Accel Partners New Data Workshop 7-14-10
Accel Partners New Data Workshop 7-14-10
 
Hadoop's Role in the Big Data Architecture, OW2con'12, Paris
Hadoop's Role in the Big Data Architecture, OW2con'12, ParisHadoop's Role in the Big Data Architecture, OW2con'12, Paris
Hadoop's Role in the Big Data Architecture, OW2con'12, Paris
 
Demonstrating the Future of Data Science
Demonstrating the Future of Data ScienceDemonstrating the Future of Data Science
Demonstrating the Future of Data Science
 
Treasure Data and Heroku
Treasure Data and HerokuTreasure Data and Heroku
Treasure Data and Heroku
 
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
GWT 2014: Emergency Conference - 02 le soluzioni geospaziali per la gestione ...
 
SoftLayer (IAAS) 22 juni 2012
SoftLayer (IAAS)   22 juni 2012SoftLayer (IAAS)   22 juni 2012
SoftLayer (IAAS) 22 juni 2012
 
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
OneCommand Vision 2.1 webcast: Cutting edge LUN SLAs, AIX on PowerPC and flex...
 
2011 04-dsi-javaee-in-the-cloud-andreadis
2011 04-dsi-javaee-in-the-cloud-andreadis2011 04-dsi-javaee-in-the-cloud-andreadis
2011 04-dsi-javaee-in-the-cloud-andreadis
 
Emergent Distributed Data Storage
Emergent Distributed Data StorageEmergent Distributed Data Storage
Emergent Distributed Data Storage
 
APAC Big Data Strategy RadhaKrishna Hiremane
APAC Big Data  Strategy RadhaKrishna  HiremaneAPAC Big Data  Strategy RadhaKrishna  Hiremane
APAC Big Data Strategy RadhaKrishna Hiremane
 
Research on big data
Research on big dataResearch on big data
Research on big data
 
Utilisation du cloud dans les systèmes intelligent
Utilisation du cloud dans les systèmes intelligentUtilisation du cloud dans les systèmes intelligent
Utilisation du cloud dans les systèmes intelligent
 
Emulex Presents Why I/O is Strategic Global Survey Results
Emulex Presents Why I/O is Strategic Global Survey ResultsEmulex Presents Why I/O is Strategic Global Survey Results
Emulex Presents Why I/O is Strategic Global Survey Results
 
Shams.khawaja
Shams.khawajaShams.khawaja
Shams.khawaja
 

Viewers also liked

Size does not matter (if your data is in a silo)
Size does not matter (if your data is in a silo)Size does not matter (if your data is in a silo)
Size does not matter (if your data is in a silo)
Ora Lassila
 
Xm Asia nokia ap digital case history
Xm Asia nokia ap digital case historyXm Asia nokia ap digital case history
Xm Asia nokia ap digital case history
Paul Soon
 
Prototyping with data at Nokia
Prototyping with data at NokiaPrototyping with data at Nokia
Prototyping with data at Nokia
Matt Biddulph
 
Marketing Analytics Research Sales Presentation
Marketing Analytics Research Sales PresentationMarketing Analytics Research Sales Presentation
Marketing Analytics Research Sales Presentation
voloe
 
Ramzi Haidamus-Nokia Technologies 2014
Ramzi Haidamus-Nokia Technologies 2014Ramzi Haidamus-Nokia Technologies 2014
Ramzi Haidamus-Nokia Technologies 2014
Dmitry Tseitlin
 
Futuro wireless wi-next_2011
Futuro wireless wi-next_2011Futuro wireless wi-next_2011
Futuro wireless wi-next_2011wi-next
 
Nokia and it's downfall
Nokia and it's downfallNokia and it's downfall
Nokia and it's downfall
Prem Kumar Bonam
 
Cognitive Cities: City analytics
Cognitive Cities: City analyticsCognitive Cities: City analytics
Cognitive Cities: City analytics
Matt Biddulph
 
5G radio access
5G radio access5G radio access
5G radio access
Ericsson
 
Big Data use cases in telcos
Big Data use cases in telcosBig Data use cases in telcos
Big Data use cases in telcos
Mohamed Zuber Khatib
 
business plan-NOKIA
business plan-NOKIAbusiness plan-NOKIA
business plan-NOKIADiya Jiang
 
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
Strata NY 2014 - Architectural considerations for Hadoop applications tutorialStrata NY 2014 - Architectural considerations for Hadoop applications tutorial
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
hadooparchbook
 
3D Users distribution for small cells network design
3D Users distribution for small cells network design3D Users distribution for small cells network design
3D Users distribution for small cells network design
Small Cell Forum
 
Arrow Global Village IoT Summit (2016)
Arrow Global Village IoT Summit (2016)Arrow Global Village IoT Summit (2016)
Arrow Global Village IoT Summit (2016)
Marc Jadoul
 
Big data architectures and the data lake
Big data architectures and the data lakeBig data architectures and the data lake
Big data architectures and the data lake
James Serra
 

Viewers also liked (15)

Size does not matter (if your data is in a silo)
Size does not matter (if your data is in a silo)Size does not matter (if your data is in a silo)
Size does not matter (if your data is in a silo)
 
Xm Asia nokia ap digital case history
Xm Asia nokia ap digital case historyXm Asia nokia ap digital case history
Xm Asia nokia ap digital case history
 
Prototyping with data at Nokia
Prototyping with data at NokiaPrototyping with data at Nokia
Prototyping with data at Nokia
 
Marketing Analytics Research Sales Presentation
Marketing Analytics Research Sales PresentationMarketing Analytics Research Sales Presentation
Marketing Analytics Research Sales Presentation
 
Ramzi Haidamus-Nokia Technologies 2014
Ramzi Haidamus-Nokia Technologies 2014Ramzi Haidamus-Nokia Technologies 2014
Ramzi Haidamus-Nokia Technologies 2014
 
Futuro wireless wi-next_2011
Futuro wireless wi-next_2011Futuro wireless wi-next_2011
Futuro wireless wi-next_2011
 
Nokia and it's downfall
Nokia and it's downfallNokia and it's downfall
Nokia and it's downfall
 
Cognitive Cities: City analytics
Cognitive Cities: City analyticsCognitive Cities: City analytics
Cognitive Cities: City analytics
 
5G radio access
5G radio access5G radio access
5G radio access
 
Big Data use cases in telcos
Big Data use cases in telcosBig Data use cases in telcos
Big Data use cases in telcos
 
business plan-NOKIA
business plan-NOKIAbusiness plan-NOKIA
business plan-NOKIA
 
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
Strata NY 2014 - Architectural considerations for Hadoop applications tutorialStrata NY 2014 - Architectural considerations for Hadoop applications tutorial
Strata NY 2014 - Architectural considerations for Hadoop applications tutorial
 
3D Users distribution for small cells network design
3D Users distribution for small cells network design3D Users distribution for small cells network design
3D Users distribution for small cells network design
 
Arrow Global Village IoT Summit (2016)
Arrow Global Village IoT Summit (2016)Arrow Global Village IoT Summit (2016)
Arrow Global Village IoT Summit (2016)
 
Big data architectures and the data lake
Big data architectures and the data lakeBig data architectures and the data lake
Big data architectures and the data lake
 

Similar to Making your Analytics Investment Pay Off - StampedeCon 2012

Hadoop as data refinery
Hadoop as data refineryHadoop as data refinery
Hadoop as data refinery
Steve Loughran
 
Hadoop as Data Refinery - Steve Loughran
Hadoop as Data Refinery - Steve LoughranHadoop as Data Refinery - Steve Loughran
Hadoop as Data Refinery - Steve Loughran
JAX London
 
From open data to API-driven business
From open data to API-driven businessFrom open data to API-driven business
From open data to API-driven business
OpenDataSoft
 
Netapp Evento Virtual Business Breakfast 20110616
Netapp Evento  Virtual  Business  Breakfast 20110616Netapp Evento  Virtual  Business  Breakfast 20110616
Netapp Evento Virtual Business Breakfast 20110616
Bruno Banha
 
Gis - open source potentials
Gis  - open source potentialsGis  - open source potentials
Gis - open source potentials
Tim Willoughby
 
[Day 3] Building Sustainable Communities
[Day 3] Building Sustainable Communities[Day 3] Building Sustainable Communities
[Day 3] Building Sustainable Communities
csi2009
 
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
Big Data Spain
 
Ensuring Mobile BI Success
Ensuring Mobile BI SuccessEnsuring Mobile BI Success
Ensuring Mobile BI Success
Birst
 
Cetas Analytics as a Service for Predictive Analytics
Cetas Analytics as a Service for Predictive AnalyticsCetas Analytics as a Service for Predictive Analytics
Cetas Analytics as a Service for Predictive Analytics
J. David Morris
 
Cetas Predictive Analytics Prezo
Cetas Predictive Analytics PrezoCetas Predictive Analytics Prezo
Cetas Predictive Analytics Prezo
Pivotal Analytics (Cetas Analytics)
 
ACES QuakeSim 2011
ACES QuakeSim 2011ACES QuakeSim 2011
ACES QuakeSim 2011
marpierc
 
Apache hadoop bigdata-in-banking
Apache hadoop bigdata-in-bankingApache hadoop bigdata-in-banking
Apache hadoop bigdata-in-banking
m_hepburn
 
BI Forum 2010 - High Performance BI: The Future of BI
BI Forum 2010 - High Performance BI: The Future of BIBI Forum 2010 - High Performance BI: The Future of BI
BI Forum 2010 - High Performance BI: The Future of BI
OKsystem
 
Big Data, Big Content, and Aligning Your Storage Strategy
Big Data, Big Content, and Aligning Your Storage StrategyBig Data, Big Content, and Aligning Your Storage Strategy
Big Data, Big Content, and Aligning Your Storage Strategy
Hitachi Vantara
 
Boosting Hadoop Performance with Emulex OneConnect® 10Gb Ethernet Adapters
Boosting Hadoop Performance with  Emulex OneConnect® 10Gb Ethernet Adapters Boosting Hadoop Performance with  Emulex OneConnect® 10Gb Ethernet Adapters
Boosting Hadoop Performance with Emulex OneConnect® 10Gb Ethernet Adapters
Emulex Corporation
 
Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案
Etu Solution
 
Secure Big Data Analytics - Hadoop & Intel
Secure Big Data Analytics - Hadoop & IntelSecure Big Data Analytics - Hadoop & Intel
Secure Big Data Analytics - Hadoop & Intel
Intel - API Security & Tokenization
 
Big Data = Big Decisions
Big Data = Big DecisionsBig Data = Big Decisions
Big Data = Big Decisions
InnoTech
 
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Big Data, Simple and Fast: Addressing the Shortcomings of HadoopBig Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Hazelcast
 
Introducing Splunk – The Big Data Engine
Introducing Splunk – The Big Data EngineIntroducing Splunk – The Big Data Engine
Introducing Splunk – The Big Data Engine
Swiss Big Data User Group
 

Similar to Making your Analytics Investment Pay Off - StampedeCon 2012 (20)

Hadoop as data refinery
Hadoop as data refineryHadoop as data refinery
Hadoop as data refinery
 
Hadoop as Data Refinery - Steve Loughran
Hadoop as Data Refinery - Steve LoughranHadoop as Data Refinery - Steve Loughran
Hadoop as Data Refinery - Steve Loughran
 
From open data to API-driven business
From open data to API-driven businessFrom open data to API-driven business
From open data to API-driven business
 
Netapp Evento Virtual Business Breakfast 20110616
Netapp Evento  Virtual  Business  Breakfast 20110616Netapp Evento  Virtual  Business  Breakfast 20110616
Netapp Evento Virtual Business Breakfast 20110616
 
Gis - open source potentials
Gis  - open source potentialsGis  - open source potentials
Gis - open source potentials
 
[Day 3] Building Sustainable Communities
[Day 3] Building Sustainable Communities[Day 3] Building Sustainable Communities
[Day 3] Building Sustainable Communities
 
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
Building a heterogeneous Hadoop Olap system with Microsoft BI stack. PABLO DO...
 
Ensuring Mobile BI Success
Ensuring Mobile BI SuccessEnsuring Mobile BI Success
Ensuring Mobile BI Success
 
Cetas Analytics as a Service for Predictive Analytics
Cetas Analytics as a Service for Predictive AnalyticsCetas Analytics as a Service for Predictive Analytics
Cetas Analytics as a Service for Predictive Analytics
 
Cetas Predictive Analytics Prezo
Cetas Predictive Analytics PrezoCetas Predictive Analytics Prezo
Cetas Predictive Analytics Prezo
 
ACES QuakeSim 2011
ACES QuakeSim 2011ACES QuakeSim 2011
ACES QuakeSim 2011
 
Apache hadoop bigdata-in-banking
Apache hadoop bigdata-in-bankingApache hadoop bigdata-in-banking
Apache hadoop bigdata-in-banking
 
BI Forum 2010 - High Performance BI: The Future of BI
BI Forum 2010 - High Performance BI: The Future of BIBI Forum 2010 - High Performance BI: The Future of BI
BI Forum 2010 - High Performance BI: The Future of BI
 
Big Data, Big Content, and Aligning Your Storage Strategy
Big Data, Big Content, and Aligning Your Storage StrategyBig Data, Big Content, and Aligning Your Storage Strategy
Big Data, Big Content, and Aligning Your Storage Strategy
 
Boosting Hadoop Performance with Emulex OneConnect® 10Gb Ethernet Adapters
Boosting Hadoop Performance with  Emulex OneConnect® 10Gb Ethernet Adapters Boosting Hadoop Performance with  Emulex OneConnect® 10Gb Ethernet Adapters
Boosting Hadoop Performance with Emulex OneConnect® 10Gb Ethernet Adapters
 
Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案Big Data 視覺化分析解決方案
Big Data 視覺化分析解決方案
 
Secure Big Data Analytics - Hadoop & Intel
Secure Big Data Analytics - Hadoop & IntelSecure Big Data Analytics - Hadoop & Intel
Secure Big Data Analytics - Hadoop & Intel
 
Big Data = Big Decisions
Big Data = Big DecisionsBig Data = Big Decisions
Big Data = Big Decisions
 
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Big Data, Simple and Fast: Addressing the Shortcomings of HadoopBig Data, Simple and Fast: Addressing the Shortcomings of Hadoop
Big Data, Simple and Fast: Addressing the Shortcomings of Hadoop
 
Introducing Splunk – The Big Data Engine
Introducing Splunk – The Big Data EngineIntroducing Splunk – The Big Data Engine
Introducing Splunk – The Big Data Engine
 

More from StampedeCon

Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
StampedeCon
 
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
StampedeCon
 
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
StampedeCon
 
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
StampedeCon
 
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
StampedeCon
 
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
StampedeCon
 
Foundations of Machine Learning - StampedeCon AI Summit 2017
Foundations of Machine Learning - StampedeCon AI Summit 2017Foundations of Machine Learning - StampedeCon AI Summit 2017
Foundations of Machine Learning - StampedeCon AI Summit 2017
StampedeCon
 
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
StampedeCon
 
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
StampedeCon
 
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
StampedeCon
 
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017
AI in the Enterprise: Past,  Present &  Future - StampedeCon AI Summit 2017AI in the Enterprise: Past,  Present &  Future - StampedeCon AI Summit 2017
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017
StampedeCon
 
A Different Data Science Approach - StampedeCon AI Summit 2017
A Different Data Science Approach - StampedeCon AI Summit 2017A Different Data Science Approach - StampedeCon AI Summit 2017
A Different Data Science Approach - StampedeCon AI Summit 2017
StampedeCon
 
Graph in Customer 360 - StampedeCon Big Data Conference 2017
Graph in Customer 360 - StampedeCon Big Data Conference 2017Graph in Customer 360 - StampedeCon Big Data Conference 2017
Graph in Customer 360 - StampedeCon Big Data Conference 2017
StampedeCon
 
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
StampedeCon
 
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
StampedeCon
 
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
StampedeCon
 
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
StampedeCon
 
Innovation in the Data Warehouse - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016Innovation in the Data Warehouse - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016
StampedeCon
 
Creating a Data Driven Organization - StampedeCon 2016
Creating a Data Driven Organization - StampedeCon 2016Creating a Data Driven Organization - StampedeCon 2016
Creating a Data Driven Organization - StampedeCon 2016
StampedeCon
 
Using The Internet of Things for Population Health Management - StampedeCon 2016
Using The Internet of Things for Population Health Management - StampedeCon 2016Using The Internet of Things for Population Health Management - StampedeCon 2016
Using The Internet of Things for Population Health Management - StampedeCon 2016
StampedeCon
 

More from StampedeCon (20)

Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
Why Should We Trust You-Interpretability of Deep Neural Networks - StampedeCo...
 
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
The Search for a New Visual Search Beyond Language - StampedeCon AI Summit 2017
 
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
Predicting Outcomes When Your Outcomes are Graphs - StampedeCon AI Summit 2017
 
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
Novel Semi-supervised Probabilistic ML Approach to SNP Variant Calling - Stam...
 
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
How to Talk about AI to Non-analaysts - Stampedecon AI Summit 2017
 
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
Getting Started with Keras and TensorFlow - StampedeCon AI Summit 2017
 
Foundations of Machine Learning - StampedeCon AI Summit 2017
Foundations of Machine Learning - StampedeCon AI Summit 2017Foundations of Machine Learning - StampedeCon AI Summit 2017
Foundations of Machine Learning - StampedeCon AI Summit 2017
 
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
Don't Start from Scratch: Transfer Learning for Novel Computer Vision Problem...
 
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
Bringing the Whole Elephant Into View Can Cognitive Systems Bring Real Soluti...
 
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
Automated AI The Next Frontier in Analytics - StampedeCon AI Summit 2017
 
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017
AI in the Enterprise: Past,  Present &  Future - StampedeCon AI Summit 2017AI in the Enterprise: Past,  Present &  Future - StampedeCon AI Summit 2017
AI in the Enterprise: Past, Present & Future - StampedeCon AI Summit 2017
 
A Different Data Science Approach - StampedeCon AI Summit 2017
A Different Data Science Approach - StampedeCon AI Summit 2017A Different Data Science Approach - StampedeCon AI Summit 2017
A Different Data Science Approach - StampedeCon AI Summit 2017
 
Graph in Customer 360 - StampedeCon Big Data Conference 2017
Graph in Customer 360 - StampedeCon Big Data Conference 2017Graph in Customer 360 - StampedeCon Big Data Conference 2017
Graph in Customer 360 - StampedeCon Big Data Conference 2017
 
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
End-to-end Big Data Projects with Python - StampedeCon Big Data Conference 2017
 
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
Doing Big Data Using Amazon's Analogs - StampedeCon Big Data Conference 2017
 
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
Enabling New Business Capabilities with Cloud-based Streaming Data Architectu...
 
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
Big Data Meets IoT: Lessons From the Cloud on Polling, Collecting, and Analyz...
 
Innovation in the Data Warehouse - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016Innovation in the Data Warehouse - StampedeCon 2016
Innovation in the Data Warehouse - StampedeCon 2016
 
Creating a Data Driven Organization - StampedeCon 2016
Creating a Data Driven Organization - StampedeCon 2016Creating a Data Driven Organization - StampedeCon 2016
Creating a Data Driven Organization - StampedeCon 2016
 
Using The Internet of Things for Population Health Management - StampedeCon 2016
Using The Internet of Things for Population Health Management - StampedeCon 2016Using The Internet of Things for Population Health Management - StampedeCon 2016
Using The Internet of Things for Population Health Management - StampedeCon 2016
 

Recently uploaded

dbms calicut university B. sc Cs 4th sem.pdf
dbms  calicut university B. sc Cs 4th sem.pdfdbms  calicut university B. sc Cs 4th sem.pdf
dbms calicut university B. sc Cs 4th sem.pdf
Shinana2
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
Jason Packer
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
SitimaJohn
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
saastr
 
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptxOperating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
Pravash Chandra Das
 
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying AheadDigital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Wask
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc
 
Azure API Management to expose backend services securely
Azure API Management to expose backend services securelyAzure API Management to expose backend services securely
Azure API Management to expose backend services securely
Dinusha Kumarasiri
 
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStrDeep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
saastr
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
shyamraj55
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Pixlogix Infotech
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Postman
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Chart Kalyan
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
panagenda
 

Recently uploaded (20)

dbms calicut university B. sc Cs 4th sem.pdf
dbms  calicut university B. sc Cs 4th sem.pdfdbms  calicut university B. sc Cs 4th sem.pdf
dbms calicut university B. sc Cs 4th sem.pdf
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
 
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
 
Operating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptxOperating System Used by Users in day-to-day life.pptx
Operating System Used by Users in day-to-day life.pptx
 
Digital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying AheadDigital Marketing Trends in 2024 | Guide for Staying Ahead
Digital Marketing Trends in 2024 | Guide for Staying Ahead
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
 
Azure API Management to expose backend services securely
Azure API Management to expose backend services securelyAzure API Management to expose backend services securely
Azure API Management to expose backend services securely
 
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStrDeep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
 
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with SlackLet's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
 
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERPBest 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
 
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfHow to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
 

Making your Analytics Investment Pay Off - StampedeCon 2012

  • 1. Making Your Analytics Click to edit Master title style Investment Pay Off Bill Eldredge Sr. Manager, Nokia Data Asset August 1, 2012 StampedeCon 1
  • 2. Overview •  Nokia and Our Big Data Challenge •  Making Hadoop Pay •  Cost Avoidance •  Product Innovation 2
  • 3. Nokia’s History: 1865 to Now 3
  • 4. The Four Cardinal W’s Location and Time Add Human Context to Social and Search WHAT WHO WHERE WHEN 4
  • 5. Great Mobile Products That Sense the World CREATE A LEADING “WHERE” PLATFORM WIN IN SMART CONNECT THE INVEST IN FUTURE DEVICES NEXT BILLION DISRUPTIONS 5
  • 6. Taking Our World Class Map Making Capabilities to the Next Level … 2.4 1500+ 400+ Validations Million Map Changes Attributes per Day 11B+ 181 Probe per Local Month Field Offices 245 Field Cars 196 Driving the Roads Countries Automatic 37k+ km Feature Roads Recognition 9 out of 10 in-car nav systems use Nokia’s Location 6 Platform
  • 7. One Platform, Enabling Contextually Rich Mobile Experiences Content Platform Maps Positions Places Directions Guidance Traffic Smart Data Apps 7
  • 8. Data, Data, Data +1B Search Queries Annually 11B Probe Points Processed Monthly 147M 24M Map Tiles Route Requests Served Daily per Month +19M Geocoder Requests Daily 55M Positioning Requests Daily 8
  • 10. Central Big Data Analytics Platform? "   One cluster for all needs? "   Do it ourselves, or with help? "   If we build it, will they come? "   Will it pay off? 10
  • 11. Phased Approach Oozie Map Reduce Pig Hive Quartz HBase Scribe HDFS HBase FTP HBase 2010 2011 2012 Build Enhance, Make It Robust… Get Data, and Make It Pay Use it Ourselves 11
  • 12. •  Nokia and Our Big Data Challenge •  Making Hadoop Pay •  Cost Avoidance •  Product Innovation 12
  • 13. Best of Both Worlds •  Key device datasets •  Key location datasets •  Dedicated reporting team •  Reporting largely self-serve •  Expensive storage (€20k/TB) •  Inexpensive storage (€2k/ TB) Key Integration Principle Aggregated Event-level Data Data Projected Savings 2012: €6 Million 13 Nokia Internal Use Only
  • 14. Integrated Solution Touch-points/Devices Consumer-Facing/ Online Serving Front-End (“Fast World”) Enterprise-Facing/ Back-End (“Slow-World”) Oracle/MySQL Reporting Activity Hadoop Cluster Hadoop Tools Logging Cluster (Cognos, Tableau) Teradata Advanced Analysis Other RDBMS Tools Streaming Data Bulk Data Sources Sources Analytics Platform Other systems 14 Nokia Internal Use Only
  • 15. Integrated Solution – Applied to Music On-device app usage Touch-points/Devices Consumer-Facing/ Online Serving Front-End (“Fast World”) Enterprise-Facing/ Back-End Improved Music (“Slow-World”) Recommendations Oracle/MySQL Reporting Activity Hadoop Cluster Hadoop Tools Logging Cluster (Cognos, Tableau) Teradata Advanced Analysis Other RDBMS Tools Streaming Download CDN Bulk Data Music Data logs & Logs Sources metadata Sources Collections Projected Benefit: Analytics Platform Other systems 3M Euros + 15 Nokia Internal Use Only
  • 16. Overview •  Nokia and Our Big Data Challenge •  Making Hadoop Pay •  Cost Avoidance •  Product Innovation 16
  • 17. The Map Is Changing… Cities are possibly our 60% greatest World’s Population in Cities in 2030 achievement, constantly RICH HUNDREDS OF MILLIONS MILLIONS evolving as we Network of Sensors of Mobile Devices of Mobile Users discover new technologies 17 Nokia Internal Use Only
  • 18. …and Needs to Become Personal… Synthesis How A of Us as Who When Individuals Living Interactions Map Where with the Real World What 18 Nokia Internal Use Only
  • 19. …to Everyone 19 Nokia Internal Use Only
  • 20. Using Different Types of Data… Asserted Observed Reference Derived Data Data Data Data …to Create a Human Motion Graph 20 Nokia Internal Use Only