SlideShare ist ein Scribd-Unternehmen logo
1 von 27
Downloaden Sie, um offline zu lesen
1
Turning Big Data Challenges into
                  Big Opportunities
                   Informatica for Big Data




                                  Wei Zheng
               Director, Product Management




2
A Little About Us – Informatica
    The #1 Independent Leader in Data Integration

•   Founded: 1993
•   2010 Revenue: $650 million         $650
•   5-year Average Growth Rate:        $600
    20% per year                       $550
•   Employees: 2,125+                  $500
                                       $450
•   Partners: 400+
     •   Major SI, ISV, OEM and        $400
         On-Demand Leaders             $350
•   Customers: 4,280+                  $300
     •   84 of Fortune 100             $250
     •   87%+ of Dow Jones             $200
     •   Government Organizations in   $150
         20 countries
                                              2005   2006   2007   2008   2009   2010
     •   # 1 in Customer Loyalty
         Rankings (5 Years in a Row)



                                                                                        3
The Informatica Platform
  Proven Value: Comprehensive, Unified, Open & Economical




Comprehensive                  Unified                      Open                  Economical

    Supporting the       Maximizing productivity    Accessing transaction or       Low Total Cost of 
    complete data         with  self‐service for     interaction data from        Ownership (TCO)  ‐
 integration lifecycle       business & IT                any source             including leveraging 
                                                                                       Hadoop
                         Adaptive data services       Mitigating risk by 
   Enabling any data      to  deliver data as a     working with what you           Fast Return on 
  integration project       service from any         have now and in the           Investment (ROI)
   Delivering at any            projects                    future               Flexible deployment 
latency from years to 
                                                                                scaled to your business 
     microseconds         Consistent interfaces 
                                                     Open to any domain,                 needs
                          across all processing 
                                                      architectural styles
                               platforms 


                                                                                                           4
Big Data on Executive Agenda
   MARCH 22, 2011, 11:30 A.M. ET.
   Report to the President: Every Federal Agency Needs a 'Big Data'
   Strategy


                                      February 4, 2011
                                      How Vendors Are Lowering Big Data
                                      Barriers

                                    March 26, 2011
                                    A Model for the Big Data Era
                                    Data-centric architecture is becoming
                                    fashionable again




     Today’s Leaders Are Racing to Uncover New Value and
  Opportunities for Competitive Insights and Improved Operations

                                                                            5
Defining Big Data
  Definition: Big data is the confluence of the three trends consisting of
  Big Transaction Data, Big Interaction Data and Big Data Processing



         BIG TRANSACTION DATA                   BIG INTERACTION DATA


           Online     Online Analytical          Social          Other
        Transaction     Processing             Media Data   Interaction Data
        Processing        (OLAP) &
          (OLTP)       DW Appliances
                                                               Call detail
                                                               records, image,
                                                               click stream data

                                                               Scientific, genomic
                                  BIG DATA INTEGRATION
                                                            Machine/Device




                                   BIG DATA PROCESSING




                                                                                     6
Value of Big Data Integration




Unleash the full business potential of Big
Data to empower the data-centric
enterprise




                                             7
Informatica 9.1: Harnessing the Power of Big
Transaction and Interaction Data


   Big Data Integration

       For All Data

    Authoritative and
    Trustworthy Data
     For All Purposes


      Self Service

      For All Users


        Adaptive
      Data Services
      For All Projects



                                               8
Big Data Integration




                       9
Big Data Integration
Gain business value from Big Data

                     Big Data Processing                           Enabling Solutions

    Extend Enterprise                                            Near-universal connectivity
    Environment with          Other Interaction data              to Big Transaction Data
             Hadoop           •Clickstream,
                              •Scientific/genomic,
         Hadoop – Web                                  Social         Connectivity to Big
                              •Sensor –
processing, text mining,                               media      Interaction Data including
                              machine/device,
    fraud/risk analytics,     •mobile, call detail                       social data
      image processing,
                              records (CDR)
   Hadoop - sandbox,          •Image files, texts                  Connectivity to Hadoop
     staging, archive

        Large scale
       processing –
       OLTP, OLAP             Your information    Big Interaction Data
New type of DW                 management
     appliances                 environment




                Big Transaction Data

                                                                                               10
Big Transaction Data
Maximize availability and performance of big transaction data


                              Uncover
                                                          Better
     Universal              new areas
                                                        Actions &
      Access               for growth &
                                                        Operations
                             efficiency

  All data including      Reliable, complete          Greater confidence
  OLTP, OLAP and              information                Continuous
   DW appliances          No data discarded               innovation


                                 Near-Universal Connectivity
                                   to Big Transaction Data




               Database                        Warehouse Appliances



                                                                           11
Big Interaction Data
Achieve a complete view with social and interaction data



 Turn insights on relationships,
                                               ?
 influences and behaviors Into
 opportunities




                                   Connectivity to Big Interaction
                                    Data including social data
                                        What                    What will she
               How                   influence
                                             Databases
                                                                 do with this
            connected                does she                   Call Detailed Records,
                                                                merchandise?
                                                                Image Files, RFIDs
             is she?               have with her                        Any
                                          Informatica MDM
                                    family and …
                                        Customer Product          additional
                                                                      External Data
                    Applications
                                      friends?                        Providers
                                                                  services?

                                                                                         12
Big Data Processing
Connectivity to Hadoop and Future Integration
                                                Predictive            Portfolio & Risk
 Sentiment             Fraud Detection                                                            Smart Devices
                                                Analytics                Analysis
  Analysis




                                            Hadoop Cluster

                                                 Graphical IDE for
                                                     Hadoop
                                                  Development



                                                                                           Future: Phase 2
                      Connectivity for
                                                                         •• Codeless & metadata driven
                                                                            Codeless & metadata driven
                      Hadoop (HDFS)                                         development
                                                                            development
                                                                         •• Prepare & integrate data on Hadoop
                                                                            Prepare & integrate data on Hadoop

 9.1 HF1 – June 2011                                                     •• Complete push down optimization
                                                                            Complete push down optimization

 •• Load data to Hadoop from any                                         •• Metadata lineage
                                                                            Metadata lineage
    Load data to Hadoop from any
    source
    source
 •• Extract data from Hadoop to any
    Extract data from Hadoop to any
   Weblogs, Mobile
    target
    target               Databases,            Semi-structured                              Cloud Applications,
  Data, Sensor Data       Data Warehouses      Unstructured      Enterprise Applications
                                                                                            Social Data


                                                                                                                  13
Authoritative and
Trustworthy Data



                    14
Multi-Style MDM
Single platform for all architectural styles and data domains


  Multi-domain                                 Multi-style
 Customer Master                                Registry
  Product Master                                 Analytic
 Chart of Accounts                            Co-existence
  Location Master       Universal MDM         Transactional
       …                                           …

Multi-deployment                                Multi-use
    Hub MDM                                   Data Integration
  Federated MDM                                Data Quality
 MDM in the Cloud                              Data Services
 MDM as a Service
                                                    …
       …

                                                                 15
Reusable Data Quality Policies
A Single Platform For Trusted Data

         Business/IT Collaboration/Data Governance
                   Role-based, Unified, Process-driven


A Single Platform For Trusted Data
One Platform for Data Integration, Data Quality and MDM

   Business       Data Stewards             Architects        Developers


       One                       One                           One
       MDM                   Data Quality                Data Integration

         Re-usable/Consistent Data Quality Rules




                                                                            16
Self-Service




               17
Self Service Data Integration

                                      SQL or Web
                                        Service

                                                                          BI Report
                                                                    b
                                                                 We
                                                             L or e
     DI Analyst                                            SQ ervic
                                                             S




                                                          Batch ETL

                                       DI Developer                     Data Warehouse


Informatica’s self-service data integration doubles productivity by eliminating
manual steps and empowering analysts to do more on their own. Analysts can
define and validate source-to-target specifications in an intuitive browser-based
tool without a data architect or DBA. On top of that, once the analyst creates the
source-to-target specification, the mapping logic is automatically generated for a
developer to deploy to production.”
Sean Hickey, Manager Data Integration, T-Mobile


                                                                                         18
Self Service
Point-of-Use Data and Context for Business Users


                        SFDC
                        Account with
                        Data
                        Controls




                                       Expand
                                       Hierarchy

                                                   Account
                                                   Hierarchy
                                                   Data Control




                                                                  19
Self Service
Pre-Built Application Accelerators to Jumpstart Projects
                                      Tag content to
            Develop mappings            augment
            based on business            project
            entities rather than        metadata
             individual tables




                                   • Improved analyst & developer
                                     productivity and collaboration
                                   • Save project time and cut costs

                                                       Jumpstart mapping




                                                                           20
Adaptive Data Services




                         21
Multi-Protocol Data Provisioning
Reusable Data Services

                         • Easily reuse DI logic/LDOs
                           for any mode/protocol
                         • Metadata-driven, visual,
                           graphical env (i.e., no-code)
                         • Execution & optimization
                           separate from design-time
                         • No re-development & re-
                           building of LDOs



                           • Reduce duplication of DI
                             development & maintenance


                                                     22
Integrated Data Quality
 Apply Data Quality Rules at Point of Access, Dynamically
                              • Provision data quality
Data                            rules via data services–
Quality   Integrated Data
v9.x.x    Quality               Read or Write
                              • Use library of templates
                                & data quality rules
                              • Auto generate data
                                quality transformations
                              • In real-time – no pre or
                                post-processing/staging

                                • Enforce data quality rules at
                                  point of access


                                                                  23
Informatica for Big Data Integration
                                                                Business Imperatives
                                                            Improve            Mergers                                                                   Increase
  Deliver           Increase              Improve                                                 Acquire &          Outsource         Governance
                                                          Efficiency &        Acquisitions                                                                Partner
 Analytical         Business             Business                                                  Retain            Non-core             Risk
                                                            Reduce                 &                                                                     Network
  Insight             Agility            Processes                                                Customers          Functions         Compliance
                                                             Costs            Divestitures                                                               Efficiency




  Big Data                                                                                        Real-Time                               Complex        Big Data
                       Ultra              Big Data          Big Data           Big Data                             Social /Big Data
Warehousing &                                                                                     Customer                                 Event        Collection &
                     messaging            Services          Archiving        Consolidation                          Synchronization
Operational BI                                                                                      View                                 Processing     Aggregation
Deliver 5x                             Saved millions    Rationalized        Unite operations    Increased           Deliver cloud     Turned human     Reduce Time
                  25% savings in                         application                             monthly slot        access to 177+                     to Market by
faster & direct                        annually by                           across 200 brands                                         review
                  data center                            portfolio and                           revenues by 4%      million                            90% by On-
access to                              improving                             over 100+                                                 into automated
                  footprint ($1M+)                       saved $1 million                        while expanding     businesses                         Boarding
customer, risk,                        trucking                              countries through                                         alerts
                  reduce latency by                      with 6 month                            target customer     worldwide and                      New Data
claims data in                         operations and                        migration of                                              in seconds
                  83 percent to 340                      payback.                                segments from       53 million                         Sources
variety of                             empowering                            business data                                             for maritime
                  microseconds,                          Reduced age of                          40 to 160 across    contacts. D&B                      Faster and
sources – DW,                          business with                         from five systems                                         security –
                  enabling a 580                         data by 87% for                         500 sources in      360 app                            enabling a
16 legacy,                             Hadoop-based                          to one                                                    through
                  percent increase                       service                                 real-time with      updates with                       wide variety
30000 data                             free-form                                                                                       geospatial and
                  in throughput over                     monitoring &                            social and          linkedin and                       of Data
marts, 10M                             questions using                                                                                 video tracking
                  1B transactions                        pattern                                 machine data        twitter                            Formats
claims via data                        sensor, mobile
                  per day and                            identification of
feeds at 1/3 of                        and geospatial
                  growing                                large scale data
the cost                               data




                                                                                                                                                                   24
Business Benefits of Informatica 9.1 for
Big Data
                 •   Big Data Integration to gain business
                     value from Big Data
                 •   Authoritative and Trustworthy Data to
                     increase business insight and consistency
                     by delivering trusted data for all purposes.
                 •   Self-Service to empower all users to
                     obtain relevant information while IT
                     remains in control
                 •   Adaptive Data Services to deliver relevant
                     data adapted to the business needs of all
                     projects




                                                                    25
26
27

Weitere ähnliche Inhalte

Was ist angesagt?

NextGen Infrastructure for Big Data
NextGen Infrastructure for Big DataNextGen Infrastructure for Big Data
NextGen Infrastructure for Big DataEd Dodds
 
Deutsche Telekom on Big Data
Deutsche Telekom on Big DataDeutsche Telekom on Big Data
Deutsche Telekom on Big DataDataWorks Summit
 
Big data ibm keynote d advani presentation
Big data ibm keynote d advani presentationBig data ibm keynote d advani presentation
Big data ibm keynote d advani presentationMassTLC
 
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Edureka!
 
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...Capgemini
 
IBM-Why Big Data?
IBM-Why Big Data?IBM-Why Big Data?
IBM-Why Big Data?Kun Le
 
Overview - IBM Big Data Platform
Overview - IBM Big Data PlatformOverview - IBM Big Data Platform
Overview - IBM Big Data PlatformVikas Manoria
 
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your life
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your lifeTop 10 ways BigInsights BigIntegrate and BigQuality will improve your life
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your lifeIBM Analytics
 
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...Cloudera, Inc.
 
DataStreams : Corporate Overview
DataStreams : Corporate OverviewDataStreams : Corporate Overview
DataStreams : Corporate OverviewDataStreams
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Denodo
 
Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013Erdenebayar Erdenebileg
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overviewoptier
 
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Denodo
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An IntroductionDenodo
 
Leveraging open source for big data stack
Leveraging open source for big data stackLeveraging open source for big data stack
Leveraging open source for big data stackFlytxt
 
Value proposition for big data isv partners 0714
Value proposition for big data isv partners 0714Value proposition for big data isv partners 0714
Value proposition for big data isv partners 0714Niu Bai
 
Record manager 8.0 presentation
Record manager 8.0  presentationRecord manager 8.0  presentation
Record manager 8.0 presentationAndrey Karpov
 
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4Frazer Clement
 

Was ist angesagt? (20)

NextGen Infrastructure for Big Data
NextGen Infrastructure for Big DataNextGen Infrastructure for Big Data
NextGen Infrastructure for Big Data
 
Deutsche Telekom on Big Data
Deutsche Telekom on Big DataDeutsche Telekom on Big Data
Deutsche Telekom on Big Data
 
Big data ibm keynote d advani presentation
Big data ibm keynote d advani presentationBig data ibm keynote d advani presentation
Big data ibm keynote d advani presentation
 
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hado...
 
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...
EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...
 
IBM-Why Big Data?
IBM-Why Big Data?IBM-Why Big Data?
IBM-Why Big Data?
 
Overview - IBM Big Data Platform
Overview - IBM Big Data PlatformOverview - IBM Big Data Platform
Overview - IBM Big Data Platform
 
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your life
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your lifeTop 10 ways BigInsights BigIntegrate and BigQuality will improve your life
Top 10 ways BigInsights BigIntegrate and BigQuality will improve your life
 
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...
Hadoop World 2011: Advancing Disney’s Data Infrastructure with Hadoop - Matt ...
 
DataStreams : Corporate Overview
DataStreams : Corporate OverviewDataStreams : Corporate Overview
DataStreams : Corporate Overview
 
Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)Introduction to Modern Data Virtualization 2021 (APAC)
Introduction to Modern Data Virtualization 2021 (APAC)
 
Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013Big Data Infrastructure and Analytics Solution on FITAT2013
Big Data Infrastructure and Analytics Solution on FITAT2013
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overview
 
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...
Implementar una estrategia eficiente de gobierno y seguridad del dato con la ...
 
Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
 
Leveraging open source for big data stack
Leveraging open source for big data stackLeveraging open source for big data stack
Leveraging open source for big data stack
 
Value proposition for big data isv partners 0714
Value proposition for big data isv partners 0714Value proposition for big data isv partners 0714
Value proposition for big data isv partners 0714
 
Record manager 8.0 presentation
Record manager 8.0  presentationRecord manager 8.0  presentation
Record manager 8.0 presentation
 
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4
200 million qps on commodity hardware : Getting started with MySQL Cluster 7.4
 
The 10 best performing data center solutions providers july 2017
The 10 best performing data center solutions providers july 2017The 10 best performing data center solutions providers july 2017
The 10 best performing data center solutions providers july 2017
 

Ähnlich wie Big Data World Forum

Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An IntroductionDenodo
 
Big data? No. Big Decisions are What You Want
Big data? No. Big Decisions are What You WantBig data? No. Big Decisions are What You Want
Big data? No. Big Decisions are What You WantStuart Miniman
 
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...Cloudera, Inc.
 
Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)
Ibm big data    hadoop summit 2012 james kobielus final 6-13-12(1)Ibm big data    hadoop summit 2012 james kobielus final 6-13-12(1)
Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)Ajay Ohri
 
ActuateOne for Utility Analytics
ActuateOne for Utility AnalyticsActuateOne for Utility Analytics
ActuateOne for Utility Analyticskatsoulis
 
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...Impulser la digitalisation et modernisation de la fonction Finance grâce à la...
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...Denodo
 
Ibm big data ibm marriage of hadoop and data warehousing
Ibm big dataibm marriage of hadoop and data warehousingIbm big dataibm marriage of hadoop and data warehousing
Ibm big data ibm marriage of hadoop and data warehousing DataWorks Summit
 
Cloud Migration Strategies that Ensure Greater Value for the Business
Cloud Migration Strategies that Ensure Greater Value for the BusinessCloud Migration Strategies that Ensure Greater Value for the Business
Cloud Migration Strategies that Ensure Greater Value for the BusinessDenodo
 
Big data appliances for BI on Cloud
Big data appliances for BI on CloudBig data appliances for BI on Cloud
Big data appliances for BI on Cloudtdwiindia
 
Hadoop: What It Is and What It's Not
Hadoop: What It Is and What It's NotHadoop: What It Is and What It's Not
Hadoop: What It Is and What It's NotInside Analysis
 
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)Mark Heid
 
Big data data lake and beyond
Big data data lake and beyond Big data data lake and beyond
Big data data lake and beyond Rajesh Kumar
 
APAC Big Data Strategy_RK
APAC Big Data Strategy_RKAPAC Big Data Strategy_RK
APAC Big Data Strategy_RKIntelAPAC
 
APAC Big Data Strategy RadhaKrishna Hiremane
APAC Big Data  Strategy RadhaKrishna  HiremaneAPAC Big Data  Strategy RadhaKrishna  Hiremane
APAC Big Data Strategy RadhaKrishna HiremaneIntelAPAC
 
Data Ninja Webinar Series: Realizing the Promise of Data Lakes
Data Ninja Webinar Series: Realizing the Promise of Data LakesData Ninja Webinar Series: Realizing the Promise of Data Lakes
Data Ninja Webinar Series: Realizing the Promise of Data LakesDenodo
 
8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshareJulianna DeLua
 
Destination Marketing Open Source and Cloud Presentation
Destination Marketing Open Source and Cloud PresentationDestination Marketing Open Source and Cloud Presentation
Destination Marketing Open Source and Cloud PresentationIsaac Christoffersen
 
Hortonworks roadshow
Hortonworks roadshowHortonworks roadshow
Hortonworks roadshowAccenture
 
Die Big Data Fabric als Enabler für Machine Learning & AI
Die Big Data Fabric als Enabler für Machine Learning & AIDie Big Data Fabric als Enabler für Machine Learning & AI
Die Big Data Fabric als Enabler für Machine Learning & AIDenodo
 

Ähnlich wie Big Data World Forum (20)

Data Virtualization: An Introduction
Data Virtualization: An IntroductionData Virtualization: An Introduction
Data Virtualization: An Introduction
 
Big data? No. Big Decisions are What You Want
Big data? No. Big Decisions are What You WantBig data? No. Big Decisions are What You Want
Big data? No. Big Decisions are What You Want
 
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...
Modernizing Your IT Infrastructure with Hadoop - Cloudera Summer Webinar Seri...
 
Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)
Ibm big data    hadoop summit 2012 james kobielus final 6-13-12(1)Ibm big data    hadoop summit 2012 james kobielus final 6-13-12(1)
Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)
 
ActuateOne for Utility Analytics
ActuateOne for Utility AnalyticsActuateOne for Utility Analytics
ActuateOne for Utility Analytics
 
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...Impulser la digitalisation et modernisation de la fonction Finance grâce à la...
Impulser la digitalisation et modernisation de la fonction Finance grâce à la...
 
Ibm big data ibm marriage of hadoop and data warehousing
Ibm big dataibm marriage of hadoop and data warehousingIbm big dataibm marriage of hadoop and data warehousing
Ibm big data ibm marriage of hadoop and data warehousing
 
Cloud Migration Strategies that Ensure Greater Value for the Business
Cloud Migration Strategies that Ensure Greater Value for the BusinessCloud Migration Strategies that Ensure Greater Value for the Business
Cloud Migration Strategies that Ensure Greater Value for the Business
 
Big data appliances for BI on Cloud
Big data appliances for BI on CloudBig data appliances for BI on Cloud
Big data appliances for BI on Cloud
 
Hadoop: What It Is and What It's Not
Hadoop: What It Is and What It's NotHadoop: What It Is and What It's Not
Hadoop: What It Is and What It's Not
 
The New Enterprise Data Platform
The New Enterprise Data PlatformThe New Enterprise Data Platform
The New Enterprise Data Platform
 
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)
Big Data Meets Social Analytics - IBM Connect 2012 (CN-CC13)
 
Big data data lake and beyond
Big data data lake and beyond Big data data lake and beyond
Big data data lake and beyond
 
APAC Big Data Strategy_RK
APAC Big Data Strategy_RKAPAC Big Data Strategy_RK
APAC Big Data Strategy_RK
 
APAC Big Data Strategy RadhaKrishna Hiremane
APAC Big Data  Strategy RadhaKrishna  HiremaneAPAC Big Data  Strategy RadhaKrishna  Hiremane
APAC Big Data Strategy RadhaKrishna Hiremane
 
Data Ninja Webinar Series: Realizing the Promise of Data Lakes
Data Ninja Webinar Series: Realizing the Promise of Data LakesData Ninja Webinar Series: Realizing the Promise of Data Lakes
Data Ninja Webinar Series: Realizing the Promise of Data Lakes
 
8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare8.17.11 big data and hadoop with informatica slideshare
8.17.11 big data and hadoop with informatica slideshare
 
Destination Marketing Open Source and Cloud Presentation
Destination Marketing Open Source and Cloud PresentationDestination Marketing Open Source and Cloud Presentation
Destination Marketing Open Source and Cloud Presentation
 
Hortonworks roadshow
Hortonworks roadshowHortonworks roadshow
Hortonworks roadshow
 
Die Big Data Fabric als Enabler für Machine Learning & AI
Die Big Data Fabric als Enabler für Machine Learning & AIDie Big Data Fabric als Enabler für Machine Learning & AI
Die Big Data Fabric als Enabler für Machine Learning & AI
 

Kürzlich hochgeladen

Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Mattias Andersson
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionDilum Bandara
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfAlex Barbosa Coqueiro
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfMounikaPolabathina
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 

Kürzlich hochgeladen (20)

Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?Are Multi-Cloud and Serverless Good or Bad?
Are Multi-Cloud and Serverless Good or Bad?
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Advanced Computer Architecture – An Introduction
Advanced Computer Architecture – An IntroductionAdvanced Computer Architecture – An Introduction
Advanced Computer Architecture – An Introduction
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Unraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdfUnraveling Multimodality with Large Language Models.pdf
Unraveling Multimodality with Large Language Models.pdf
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
What is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdfWhat is DBT - The Ultimate Data Build Tool.pdf
What is DBT - The Ultimate Data Build Tool.pdf
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 

Big Data World Forum

  • 1. 1
  • 2. Turning Big Data Challenges into Big Opportunities Informatica for Big Data Wei Zheng Director, Product Management 2
  • 3. A Little About Us – Informatica The #1 Independent Leader in Data Integration • Founded: 1993 • 2010 Revenue: $650 million $650 • 5-year Average Growth Rate: $600 20% per year $550 • Employees: 2,125+ $500 $450 • Partners: 400+ • Major SI, ISV, OEM and $400 On-Demand Leaders $350 • Customers: 4,280+ $300 • 84 of Fortune 100 $250 • 87%+ of Dow Jones $200 • Government Organizations in $150 20 countries 2005 2006 2007 2008 2009 2010 • # 1 in Customer Loyalty Rankings (5 Years in a Row) 3
  • 4. The Informatica Platform Proven Value: Comprehensive, Unified, Open & Economical Comprehensive Unified Open Economical Supporting the  Maximizing productivity  Accessing transaction or  Low Total Cost of  complete data  with  self‐service for  interaction data from  Ownership (TCO)  ‐ integration lifecycle business & IT any source including leveraging  Hadoop Adaptive data services  Mitigating risk by  Enabling any data  to  deliver data as a  working with what you  Fast Return on  integration project service from any  have now and in the  Investment (ROI) Delivering at any  projects future   Flexible deployment  latency from years to  scaled to your business  microseconds Consistent interfaces  Open to any domain,  needs across all processing  architectural styles platforms  4
  • 5. Big Data on Executive Agenda MARCH 22, 2011, 11:30 A.M. ET. Report to the President: Every Federal Agency Needs a 'Big Data' Strategy February 4, 2011 How Vendors Are Lowering Big Data Barriers March 26, 2011 A Model for the Big Data Era Data-centric architecture is becoming fashionable again Today’s Leaders Are Racing to Uncover New Value and Opportunities for Competitive Insights and Improved Operations 5
  • 6. Defining Big Data Definition: Big data is the confluence of the three trends consisting of Big Transaction Data, Big Interaction Data and Big Data Processing BIG TRANSACTION DATA BIG INTERACTION DATA Online Online Analytical Social Other Transaction Processing Media Data Interaction Data Processing (OLAP) & (OLTP) DW Appliances Call detail records, image, click stream data Scientific, genomic BIG DATA INTEGRATION Machine/Device BIG DATA PROCESSING 6
  • 7. Value of Big Data Integration Unleash the full business potential of Big Data to empower the data-centric enterprise 7
  • 8. Informatica 9.1: Harnessing the Power of Big Transaction and Interaction Data Big Data Integration For All Data Authoritative and Trustworthy Data For All Purposes Self Service For All Users Adaptive Data Services For All Projects 8
  • 10. Big Data Integration Gain business value from Big Data Big Data Processing Enabling Solutions Extend Enterprise Near-universal connectivity Environment with Other Interaction data to Big Transaction Data Hadoop •Clickstream, •Scientific/genomic, Hadoop – Web Social Connectivity to Big •Sensor – processing, text mining, media Interaction Data including machine/device, fraud/risk analytics, •mobile, call detail social data image processing, records (CDR) Hadoop - sandbox, •Image files, texts Connectivity to Hadoop staging, archive Large scale processing – OLTP, OLAP Your information Big Interaction Data New type of DW management appliances environment Big Transaction Data 10
  • 11. Big Transaction Data Maximize availability and performance of big transaction data Uncover Better Universal new areas Actions & Access for growth & Operations efficiency All data including Reliable, complete Greater confidence OLTP, OLAP and information Continuous DW appliances No data discarded innovation Near-Universal Connectivity to Big Transaction Data Database Warehouse Appliances 11
  • 12. Big Interaction Data Achieve a complete view with social and interaction data Turn insights on relationships, ? influences and behaviors Into opportunities Connectivity to Big Interaction Data including social data What What will she How influence Databases do with this connected does she Call Detailed Records, merchandise? Image Files, RFIDs is she? have with her Any Informatica MDM family and … Customer Product additional External Data Applications friends? Providers services? 12
  • 13. Big Data Processing Connectivity to Hadoop and Future Integration Predictive Portfolio & Risk Sentiment Fraud Detection Smart Devices Analytics Analysis Analysis Hadoop Cluster Graphical IDE for Hadoop Development Future: Phase 2 Connectivity for •• Codeless & metadata driven Codeless & metadata driven Hadoop (HDFS) development development •• Prepare & integrate data on Hadoop Prepare & integrate data on Hadoop 9.1 HF1 – June 2011 •• Complete push down optimization Complete push down optimization •• Load data to Hadoop from any •• Metadata lineage Metadata lineage Load data to Hadoop from any source source •• Extract data from Hadoop to any Extract data from Hadoop to any Weblogs, Mobile target target Databases, Semi-structured Cloud Applications, Data, Sensor Data Data Warehouses Unstructured Enterprise Applications Social Data 13
  • 15. Multi-Style MDM Single platform for all architectural styles and data domains Multi-domain Multi-style Customer Master Registry Product Master Analytic Chart of Accounts Co-existence Location Master Universal MDM Transactional … … Multi-deployment Multi-use Hub MDM Data Integration Federated MDM Data Quality MDM in the Cloud Data Services MDM as a Service … … 15
  • 16. Reusable Data Quality Policies A Single Platform For Trusted Data Business/IT Collaboration/Data Governance Role-based, Unified, Process-driven A Single Platform For Trusted Data One Platform for Data Integration, Data Quality and MDM Business Data Stewards Architects Developers One One One MDM Data Quality Data Integration Re-usable/Consistent Data Quality Rules 16
  • 18. Self Service Data Integration SQL or Web Service BI Report b We L or e DI Analyst SQ ervic S Batch ETL DI Developer Data Warehouse Informatica’s self-service data integration doubles productivity by eliminating manual steps and empowering analysts to do more on their own. Analysts can define and validate source-to-target specifications in an intuitive browser-based tool without a data architect or DBA. On top of that, once the analyst creates the source-to-target specification, the mapping logic is automatically generated for a developer to deploy to production.” Sean Hickey, Manager Data Integration, T-Mobile 18
  • 19. Self Service Point-of-Use Data and Context for Business Users SFDC Account with Data Controls Expand Hierarchy Account Hierarchy Data Control 19
  • 20. Self Service Pre-Built Application Accelerators to Jumpstart Projects Tag content to Develop mappings augment based on business project entities rather than metadata individual tables • Improved analyst & developer productivity and collaboration • Save project time and cut costs Jumpstart mapping 20
  • 22. Multi-Protocol Data Provisioning Reusable Data Services • Easily reuse DI logic/LDOs for any mode/protocol • Metadata-driven, visual, graphical env (i.e., no-code) • Execution & optimization separate from design-time • No re-development & re- building of LDOs • Reduce duplication of DI development & maintenance 22
  • 23. Integrated Data Quality Apply Data Quality Rules at Point of Access, Dynamically • Provision data quality Data rules via data services– Quality Integrated Data v9.x.x Quality Read or Write • Use library of templates & data quality rules • Auto generate data quality transformations • In real-time – no pre or post-processing/staging • Enforce data quality rules at point of access 23
  • 24. Informatica for Big Data Integration Business Imperatives Improve Mergers Increase Deliver Increase Improve Acquire & Outsource Governance Efficiency & Acquisitions Partner Analytical Business Business Retain Non-core Risk Reduce & Network Insight Agility Processes Customers Functions Compliance Costs Divestitures Efficiency Big Data Real-Time Complex Big Data Ultra Big Data Big Data Big Data Social /Big Data Warehousing & Customer Event Collection & messaging Services Archiving Consolidation Synchronization Operational BI View Processing Aggregation Deliver 5x Saved millions Rationalized Unite operations Increased Deliver cloud Turned human Reduce Time 25% savings in application monthly slot access to 177+ to Market by faster & direct annually by across 200 brands review data center portfolio and revenues by 4% million 90% by On- access to improving over 100+ into automated footprint ($1M+) saved $1 million while expanding businesses Boarding customer, risk, trucking countries through alerts reduce latency by with 6 month target customer worldwide and New Data claims data in operations and migration of in seconds 83 percent to 340 payback. segments from 53 million Sources variety of empowering business data for maritime microseconds, Reduced age of 40 to 160 across contacts. D&B Faster and sources – DW, business with from five systems security – enabling a 580 data by 87% for 500 sources in 360 app enabling a 16 legacy, Hadoop-based to one through percent increase service real-time with updates with wide variety 30000 data free-form geospatial and in throughput over monitoring & social and linkedin and of Data marts, 10M questions using video tracking 1B transactions pattern machine data twitter Formats claims via data sensor, mobile per day and identification of feeds at 1/3 of and geospatial growing large scale data the cost data 24
  • 25. Business Benefits of Informatica 9.1 for Big Data • Big Data Integration to gain business value from Big Data • Authoritative and Trustworthy Data to increase business insight and consistency by delivering trusted data for all purposes. • Self-Service to empower all users to obtain relevant information while IT remains in control • Adaptive Data Services to deliver relevant data adapted to the business needs of all projects 25
  • 26. 26
  • 27. 27