SlideShare a Scribd company logo
1 of 17
Introducing the next
generation in data
w arehousing
HP Neoviewenterprise
data warehousing platform
M Low Hon Chau
 r.      ,
Regional S. Director, FSI (SE. Asia)
Hew Packard
    lett


                            Banking 2007 – Vie tnam (May 30 – June 1, 2007)




© 2007 Hew   lett-Packard Developm Com
                                    ent      pany, L.P.
The inform 1, 2009
1      Julyation contained herein is subject to change without notice
Discussion topics
                   •   Industry trends and market dynam ics
                        − The need for better business outcomes
                       −   Relevance to Banking in Vietnam

                   •   A newapproach to data w  arehousing
                        − Em pow ering decision-making
                        − and action taking


                   •   The HP Neoviewplatform
                       − Designed for the next generation in data
                         warehousing

                   •   Questions & Answers



2   July 1, 2009
BI & Data Warehouse –Vietnam
Context
                         •
                         Large Country, Population
                         •
                         Cash-Based, now
                         •
                         Future –Internet Banking
                         •
                         Credit Cards, Cash Less, Int’
                                                     l
                         •
                         Lots of Data Generated
                         •
                         Account Data, Transaction Data
                         •
                         Financial Data
                         •
                         Demographic Data
                         •
                         Psychographics Data
                         •
                         Consum Data & Business
                               er
                         Data


                         What are the Banks g o ing to
                         do with all the s e Data ?

Pho to c re dit: USAID

3     July 1, 2009
Traditional data warehouse approach
                   Physical            Index
    Analyze                   Load               Query   Ongoing
                   database             and
     data                     data               data     tuning
                    design           aggregate



• Performance is dependent on getting a good
  physical design
• Tim to m
     e      arket w newdata is lim
                   ith               ited by skills
  and resources
• Query perform ance is poor when queries don’   t
  take advantage of the design (no index scans)



4   July 1, 2009
Howdo w use the data/Inform
       e                   ation ?



                                   Healthcare             Retail                Com unication
                                                                                     m
Banking                                                                         s
                                                                                m edia and
s e rvic e s                                                                    entertainment
•   Ris k manag e me nt            •   Patient outcomes • Wallet share          •   Digital content
•   Bas e l II c o mplianc e       •   Case correlation   •   Real-time             integration
•   Cus to me r pro fitability     •   Single view            decision-making   •   Custom  er
                                                          •   Supply chain          retention
•   Cus to me r S atis fac tio n   •   Regulations for
                                       patient records        optimization      •   Fraud
•   Be at Co mpe titio n
                                       (HIPAA)                                  •   Billing records
                                                                                    and call logs
                      Broad governm regulations: (Country Specific)
                                   ent

5      July 1, 2009
W Happens W
 hat       hen the Situation Becom Com
                                  es  plex?
                                      Treasury

                                      Retail Banking

                                      Corp. Banking

                                      Trade Finance
                                      Asset
                                      Management
                                      Internet Banking

                                      Credit Card
                                      Bank HR

                                      M t Reporting
                                       gm

                                      SBV Reporting
                                      And?

                                      And?
     Pho to c re dit: USAID
                                      And?
                                      And?
6   July 1, 2009
Market dynamics
• BI     is becom m strategic to the business
                 ing ore
     − 2007 Gartner survey of CIOs ranked BI applications as #1 priority for
       second year running (Business Intelligence Market Dynam  ics, M arch 2007)
• Information capacity continues to grow
     − According to IDC, total storage capacity is expected to growat a CAGR of
       67% betw  een nowand 2010, from roughly 6,000 petabytes today to m   ore
       than 27,000 petabytes in 2010 (“ W
                                        IDC orldw IT Spending 2006–
                                                     ide                  2010
       Forecast: The Worldw Black Book, Version 1,”
                             ide                         2006)
    “Integrating non-m  ission-critical data warehouses w m
                                                         ith ission-critical
    system creates an unm
            s                 anaged point of failure” Tim to GrowUp:
                                                      (“ e
    The M  odern, M ission-Critical Data Warehouse,”  Gartner, March 2007)




7    July 1, 2009
A newapproach
to data
w arehousing
Em  powering decision-making and action
taking




8   July 1, 2009
Business intelligence is evolving to
becom an integral part of business
       e
operations
•   Strategic                         •   Operational
•   Reporting                         •   Autom  ating action
•   Standalone                        •   M ission-critical com ponent
•   Weekly batch updates              •   Continuous online updates
•   Sim ETL
        ple                           •   Sophisticated data integration
•   Single-function departm   ental   •   EDW supporting “    single version of
    data m arts                           the truth” m
                                                     for ultiple applications
•   Fewusers doing strategic          •   Thousands of users perform     ing
    analysis                              m any types of tasks
•   Data volum < 1 TB
                 e                    •   Data volum at m than 100 TB
                                                       es      ore
•   Response tim and
                   e                  •   Near-real-tim response and 24x7
                                                         e
    availability not critical             online everything
•   Sum arized data
         m                            •   Detail plus years of history



9    July 1, 2009
Business technology portfolio
Technology for better business outcomes

                                                         Provide good inform ation to
               Business Information Optimization         produce better business
                                                         decisions

               Business Technology Optimization
                                                         Low risk to the enterprise
                                                              er
                                                         w better control of the
                                                           ith
                                                         infrastructure
                    Adaptive infrastructure


                                                         Reduce the cost of IT while
     Servers and
                           Services           Software   delivering m to the
                                                                     ore
       storage
                                                         business



10   July 1, 2009
The HP Neoview
platform answ the             ers
call
Designed for the next generation




11   July 1, 2009
W is the Neoviewplatform
 hat                    ?
• An integrated hardw and softw platform for EDW designed to
                        are            are
  support terabytes of data and up to 256 processors
• A com   plete, preconfigured solution that can be rapidly deployed, easily
  m anaged, and is com    patible w existing BI applications and system
                                   ith                                   s
• Built from redeployable standards-based hardw     are com  ponents


         ETL tools                                                       Query tools


                                                            Standard
                            Load/unload    Integrated       interface
                             software       hardw are        softw are
                                               OS
               IBM W  ebSphere
          Information Integration            DBM  S
                                          Managem console
                                                 ent


12   July 1, 2009
HP Neoviewplatform
The next-generation enterprise data warehouse

                                                Simplicity and lower
     Enterprise-class                           TCO




                                         +
     capabilities                                ●
                                                     Com odity platform
                                                           m
     ●
          High-perform    ance,                  ●
                                                     Prebalanced,
          m assively parallel                        preconfigured, and
          database                                   pretested
     ●
          Handles com     plex queries           ●
                                                     Easy to incorporate into
          and m  ultiple users                       existing environm   ents
     ●
          Readily scales to 100s                 ●
                                                     Rem   otely m onitored and
          of processors                              m anaged by HP
     ●
          Built-in fault tolerance               ●
                                                     Reduced operating and
                                                     adm  inistration overhead
                              Data warehouse platform
                        Surrounded by world-class HP services
13       July 1, 2009
Industry-standard components offer
     better value
 BI client




                             Gigabit Ethernet




                                                    ….
ETL clients




                                                                                   ….
                                                                  Sw itch
                                                                  fabric


                                                                                   HP
                                                HP Integrity   HP ServerNet
                                                                              StorageW orks
                                                 servers        technology
                                                                              Fibre Channel
                                                                                  disks


     14       July 1, 2009
The Neoviewarchitecture is enhanced
     for decision support
                             •   Shared-nothing MPP
                                  ●
                                      Each processor is a unit of parallel work
 BI client




                                  ●
                                      Transparent softw virtualization
                                                       are
                             •   Database virtualization
                                  ●
                                      Data is transparently hashed across all disks
                                  ●
                                      Balances I/O activity and processor utilization
                             •   Parallel query execution
                                  ●
                                      Queries are divided into subtasks and executed in parallel with
                                      results stream through m ory
                                                    ed           em
                                  ●
                                      Execution is pushed dow to low softw level
                                                               n      est      are
                                 Fault tolerant
ETL clients




                             •
                                  ●
                                      Platform is available 24x7 in spite of any single point of
                                      hardw or softw failure
                                            are          are
                                  ●
                                      Leverages 30 years of HP NonStop engineering
                             •   Extrem processing pow
                                       e              er
                                  ●
                                      1 Intel® Itanium 2 processor to tw pairs of RAID 1 drives
                                                      ®
                                                                        o




     15       July 1, 2009
Neoviewplatform is designed to meet
next-generation needs
                Architectural feature                        Key custom benefit
                                                                       er
 M assively parallel processing across hundreds   Consistent high performance in mixed workloads
 of processors

 Shared-nothing architecture                      Linear scalability without bottlenecks or limits

 Advanced parallel query optimizer                Fast processing of complex queries

 Built-in fault tolerance                         Superior availability without extra cost or
                                                  managem overhead
                                                            ent

 Rem m
    ote anagem and m
              ent   onitoring from HP             Simplified administration and reduced risk


 Industry-standard components                     Investm protection and easier data center
                                                           ent
                                                  integration
     High ratio of processing pow to storage
                                 er               Reduced need for indexes and sum ary tables
                                                                                  m

     Com pletely integrated hardware, software,   Faster tim to benefit
                                                            e
     and services


16     July 1, 2009
“ tests processing tens of terabytes
In
 of data in parallel across as m      any as
 256 processors, the HP Neoview
 platform delivers im      pressive
 perform       ance at a surprisingly
 attractive price… .”
 Richard Winter
     President, WinterCorp




17    July 1, 2009

More Related Content

Similar to Low Hon Chau

OSC2012: Big Data Using Open Source: Netapp Project - Technical
OSC2012: Big Data Using Open Source: Netapp Project - TechnicalOSC2012: Big Data Using Open Source: Netapp Project - Technical
OSC2012: Big Data Using Open Source: Netapp Project - TechnicalAccenture the Netherlands
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overviewoptier
 
Big Data: A Big Trap for Product Development
Big Data: A Big Trap for Product DevelopmentBig Data: A Big Trap for Product Development
Big Data: A Big Trap for Product DevelopmentStrategy 2 Market, Inc,
 
OpTier McKinsey Big Data Overview
OpTier McKinsey Big Data OverviewOpTier McKinsey Big Data Overview
OpTier McKinsey Big Data Overviewnickychu
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overviewoptier
 
Enterprise Reporting Journey at Merial
Enterprise Reporting Journey at MerialEnterprise Reporting Journey at Merial
Enterprise Reporting Journey at MerialArvind Purushothaman
 
Finding fraud in large, diverse data sets
Finding fraud in large, diverse data setsFinding fraud in large, diverse data sets
Finding fraud in large, diverse data setsChris Selland
 
Webinar | Using Hadoop Analytics to Gain a Big Data Advantage
Webinar | Using Hadoop Analytics to Gain a Big Data AdvantageWebinar | Using Hadoop Analytics to Gain a Big Data Advantage
Webinar | Using Hadoop Analytics to Gain a Big Data AdvantageCloudera, Inc.
 
Information på agendaen
Information på agendaenInformation på agendaen
Information på agendaenIBM Danmark
 
Big Data and Implications on Platform Architecture
Big Data and Implications on Platform ArchitectureBig Data and Implications on Platform Architecture
Big Data and Implications on Platform ArchitectureOdinot Stanislas
 
Agile BI : meeting the best of both worlds from departmental and enterprise BI
Agile BI : meeting the best of both worlds from departmental and enterprise BIAgile BI : meeting the best of both worlds from departmental and enterprise BI
Agile BI : meeting the best of both worlds from departmental and enterprise BIJean-Michel Franco
 
Rob anderson
Rob andersonRob anderson
Rob andersonEduserv
 
Intel Cloud summit: Big Data by Nick Knupffer
Intel Cloud summit: Big Data by Nick KnupfferIntel Cloud summit: Big Data by Nick Knupffer
Intel Cloud summit: Big Data by Nick KnupfferIntelAPAC
 
Understanding The Big Data Opportunity Final
Understanding The Big Data Opportunity FinalUnderstanding The Big Data Opportunity Final
Understanding The Big Data Opportunity FinalAndrew Gregoris
 
Demystifying BI For Mid-Market Enterprises
Demystifying BI For Mid-Market EnterprisesDemystifying BI For Mid-Market Enterprises
Demystifying BI For Mid-Market EnterprisesJamal_Shah
 
Kim Escherich - How Big Data Transforms Our World
Kim Escherich - How Big Data Transforms Our WorldKim Escherich - How Big Data Transforms Our World
Kim Escherich - How Big Data Transforms Our WorldBigDataViz
 
How to Enable Personalized Marketing Even Before 'Big Data'
How to Enable Personalized Marketing Even Before 'Big Data'How to Enable Personalized Marketing Even Before 'Big Data'
How to Enable Personalized Marketing Even Before 'Big Data'DocuStar
 
BusinessDecision - Know Your Market
BusinessDecision - Know Your MarketBusinessDecision - Know Your Market
BusinessDecision - Know Your MarketVijay Harrell
 

Similar to Low Hon Chau (20)

OSC2012: Big Data Using Open Source: Netapp Project - Technical
OSC2012: Big Data Using Open Source: Netapp Project - TechnicalOSC2012: Big Data Using Open Source: Netapp Project - Technical
OSC2012: Big Data Using Open Source: Netapp Project - Technical
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overview
 
Big Data: A Big Trap for Product Development
Big Data: A Big Trap for Product DevelopmentBig Data: A Big Trap for Product Development
Big Data: A Big Trap for Product Development
 
OpTier McKinsey Big Data Overview
OpTier McKinsey Big Data OverviewOpTier McKinsey Big Data Overview
OpTier McKinsey Big Data Overview
 
McKinsey Big Data Overview
McKinsey Big Data OverviewMcKinsey Big Data Overview
McKinsey Big Data Overview
 
Cognos Presentation Gartner BI
Cognos Presentation Gartner BICognos Presentation Gartner BI
Cognos Presentation Gartner BI
 
Enterprise Reporting Journey at Merial
Enterprise Reporting Journey at MerialEnterprise Reporting Journey at Merial
Enterprise Reporting Journey at Merial
 
Finding fraud in large, diverse data sets
Finding fraud in large, diverse data setsFinding fraud in large, diverse data sets
Finding fraud in large, diverse data sets
 
Webinar | Using Hadoop Analytics to Gain a Big Data Advantage
Webinar | Using Hadoop Analytics to Gain a Big Data AdvantageWebinar | Using Hadoop Analytics to Gain a Big Data Advantage
Webinar | Using Hadoop Analytics to Gain a Big Data Advantage
 
Technology in Microfinance
Technology in Microfinance Technology in Microfinance
Technology in Microfinance
 
Information på agendaen
Information på agendaenInformation på agendaen
Information på agendaen
 
Big Data and Implications on Platform Architecture
Big Data and Implications on Platform ArchitectureBig Data and Implications on Platform Architecture
Big Data and Implications on Platform Architecture
 
Agile BI : meeting the best of both worlds from departmental and enterprise BI
Agile BI : meeting the best of both worlds from departmental and enterprise BIAgile BI : meeting the best of both worlds from departmental and enterprise BI
Agile BI : meeting the best of both worlds from departmental and enterprise BI
 
Rob anderson
Rob andersonRob anderson
Rob anderson
 
Intel Cloud summit: Big Data by Nick Knupffer
Intel Cloud summit: Big Data by Nick KnupfferIntel Cloud summit: Big Data by Nick Knupffer
Intel Cloud summit: Big Data by Nick Knupffer
 
Understanding The Big Data Opportunity Final
Understanding The Big Data Opportunity FinalUnderstanding The Big Data Opportunity Final
Understanding The Big Data Opportunity Final
 
Demystifying BI For Mid-Market Enterprises
Demystifying BI For Mid-Market EnterprisesDemystifying BI For Mid-Market Enterprises
Demystifying BI For Mid-Market Enterprises
 
Kim Escherich - How Big Data Transforms Our World
Kim Escherich - How Big Data Transforms Our WorldKim Escherich - How Big Data Transforms Our World
Kim Escherich - How Big Data Transforms Our World
 
How to Enable Personalized Marketing Even Before 'Big Data'
How to Enable Personalized Marketing Even Before 'Big Data'How to Enable Personalized Marketing Even Before 'Big Data'
How to Enable Personalized Marketing Even Before 'Big Data'
 
BusinessDecision - Know Your Market
BusinessDecision - Know Your MarketBusinessDecision - Know Your Market
BusinessDecision - Know Your Market
 

Recently uploaded

Fordham -How effective decision-making is within the IT department - Analysis...
Fordham -How effective decision-making is within the IT department - Analysis...Fordham -How effective decision-making is within the IT department - Analysis...
Fordham -How effective decision-making is within the IT department - Analysis...Peter Ward
 
Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessSeta Wicaksana
 
Ten Organizational Design Models to align structure and operations to busines...
Ten Organizational Design Models to align structure and operations to busines...Ten Organizational Design Models to align structure and operations to busines...
Ten Organizational Design Models to align structure and operations to busines...Seta Wicaksana
 
Church Building Grants To Assist With New Construction, Additions, And Restor...
Church Building Grants To Assist With New Construction, Additions, And Restor...Church Building Grants To Assist With New Construction, Additions, And Restor...
Church Building Grants To Assist With New Construction, Additions, And Restor...Americas Got Grants
 
Memorándum de Entendimiento (MoU) entre Codelco y SQM
Memorándum de Entendimiento (MoU) entre Codelco y SQMMemorándum de Entendimiento (MoU) entre Codelco y SQM
Memorándum de Entendimiento (MoU) entre Codelco y SQMVoces Mineras
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607dollysharma2066
 
Darshan Hiranandani [News About Next CEO].pdf
Darshan Hiranandani [News About Next CEO].pdfDarshan Hiranandani [News About Next CEO].pdf
Darshan Hiranandani [News About Next CEO].pdfShashank Mehta
 
Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Kirill Klimov
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607dollysharma2066
 
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCRashishs7044
 
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCRashishs7044
 
8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCRashishs7044
 
Market Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMarket Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMintel Group
 
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!Doge Mining Website
 
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckPitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckHajeJanKamps
 
TriStar Gold Corporate Presentation - April 2024
TriStar Gold Corporate Presentation - April 2024TriStar Gold Corporate Presentation - April 2024
TriStar Gold Corporate Presentation - April 2024Adnet Communications
 
Innovation Conference 5th March 2024.pdf
Innovation Conference 5th March 2024.pdfInnovation Conference 5th March 2024.pdf
Innovation Conference 5th March 2024.pdfrichard876048
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaoncallgirls2057
 

Recently uploaded (20)

Fordham -How effective decision-making is within the IT department - Analysis...
Fordham -How effective decision-making is within the IT department - Analysis...Fordham -How effective decision-making is within the IT department - Analysis...
Fordham -How effective decision-making is within the IT department - Analysis...
 
Organizational Structure Running A Successful Business
Organizational Structure Running A Successful BusinessOrganizational Structure Running A Successful Business
Organizational Structure Running A Successful Business
 
Ten Organizational Design Models to align structure and operations to busines...
Ten Organizational Design Models to align structure and operations to busines...Ten Organizational Design Models to align structure and operations to busines...
Ten Organizational Design Models to align structure and operations to busines...
 
Church Building Grants To Assist With New Construction, Additions, And Restor...
Church Building Grants To Assist With New Construction, Additions, And Restor...Church Building Grants To Assist With New Construction, Additions, And Restor...
Church Building Grants To Assist With New Construction, Additions, And Restor...
 
Memorándum de Entendimiento (MoU) entre Codelco y SQM
Memorándum de Entendimiento (MoU) entre Codelco y SQMMemorándum de Entendimiento (MoU) entre Codelco y SQM
Memorándum de Entendimiento (MoU) entre Codelco y SQM
 
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607FULL ENJOY Call girls in Paharganj Delhi | 8377087607
FULL ENJOY Call girls in Paharganj Delhi | 8377087607
 
Call Us ➥9319373153▻Call Girls In North Goa
Call Us ➥9319373153▻Call Girls In North GoaCall Us ➥9319373153▻Call Girls In North Goa
Call Us ➥9319373153▻Call Girls In North Goa
 
Corporate Profile 47Billion Information Technology
Corporate Profile 47Billion Information TechnologyCorporate Profile 47Billion Information Technology
Corporate Profile 47Billion Information Technology
 
Darshan Hiranandani [News About Next CEO].pdf
Darshan Hiranandani [News About Next CEO].pdfDarshan Hiranandani [News About Next CEO].pdf
Darshan Hiranandani [News About Next CEO].pdf
 
Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024Flow Your Strategy at Flight Levels Day 2024
Flow Your Strategy at Flight Levels Day 2024
 
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
(Best) ENJOY Call Girls in Faridabad Ex | 8377087607
 
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
8447779800, Low rate Call girls in Kotla Mubarakpur Delhi NCR
 
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
8447779800, Low rate Call girls in Shivaji Enclave Delhi NCR
 
8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR8447779800, Low rate Call girls in Rohini Delhi NCR
8447779800, Low rate Call girls in Rohini Delhi NCR
 
Market Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 EditionMarket Sizes Sample Report - 2024 Edition
Market Sizes Sample Report - 2024 Edition
 
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!
Unlocking the Future: Explore Web 3.0 Workshop to Start Earning Today!
 
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deckPitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
Pitch Deck Teardown: Geodesic.Life's $500k Pre-seed deck
 
TriStar Gold Corporate Presentation - April 2024
TriStar Gold Corporate Presentation - April 2024TriStar Gold Corporate Presentation - April 2024
TriStar Gold Corporate Presentation - April 2024
 
Innovation Conference 5th March 2024.pdf
Innovation Conference 5th March 2024.pdfInnovation Conference 5th March 2024.pdf
Innovation Conference 5th March 2024.pdf
 
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City GurgaonCall Us 📲8800102216📞 Call Girls In DLF City Gurgaon
Call Us 📲8800102216📞 Call Girls In DLF City Gurgaon
 

Low Hon Chau

  • 1. Introducing the next generation in data w arehousing HP Neoviewenterprise data warehousing platform M Low Hon Chau r. , Regional S. Director, FSI (SE. Asia) Hew Packard lett Banking 2007 – Vie tnam (May 30 – June 1, 2007) © 2007 Hew lett-Packard Developm Com ent pany, L.P. The inform 1, 2009 1 Julyation contained herein is subject to change without notice
  • 2. Discussion topics • Industry trends and market dynam ics − The need for better business outcomes − Relevance to Banking in Vietnam • A newapproach to data w arehousing − Em pow ering decision-making − and action taking • The HP Neoviewplatform − Designed for the next generation in data warehousing • Questions & Answers 2 July 1, 2009
  • 3. BI & Data Warehouse –Vietnam Context • Large Country, Population • Cash-Based, now • Future –Internet Banking • Credit Cards, Cash Less, Int’ l • Lots of Data Generated • Account Data, Transaction Data • Financial Data • Demographic Data • Psychographics Data • Consum Data & Business er Data What are the Banks g o ing to do with all the s e Data ? Pho to c re dit: USAID 3 July 1, 2009
  • 4. Traditional data warehouse approach Physical Index Analyze Load Query Ongoing database and data data data tuning design aggregate • Performance is dependent on getting a good physical design • Tim to m e arket w newdata is lim ith ited by skills and resources • Query perform ance is poor when queries don’ t take advantage of the design (no index scans) 4 July 1, 2009
  • 5. Howdo w use the data/Inform e ation ? Healthcare Retail Com unication m Banking s m edia and s e rvic e s entertainment • Ris k manag e me nt • Patient outcomes • Wallet share • Digital content • Bas e l II c o mplianc e • Case correlation • Real-time integration • Cus to me r pro fitability • Single view decision-making • Custom er • Supply chain retention • Cus to me r S atis fac tio n • Regulations for patient records optimization • Fraud • Be at Co mpe titio n (HIPAA) • Billing records and call logs Broad governm regulations: (Country Specific) ent 5 July 1, 2009
  • 6. W Happens W hat hen the Situation Becom Com es plex? Treasury Retail Banking Corp. Banking Trade Finance Asset Management Internet Banking Credit Card Bank HR M t Reporting gm SBV Reporting And? And? Pho to c re dit: USAID And? And? 6 July 1, 2009
  • 7. Market dynamics • BI is becom m strategic to the business ing ore − 2007 Gartner survey of CIOs ranked BI applications as #1 priority for second year running (Business Intelligence Market Dynam ics, M arch 2007) • Information capacity continues to grow − According to IDC, total storage capacity is expected to growat a CAGR of 67% betw een nowand 2010, from roughly 6,000 petabytes today to m ore than 27,000 petabytes in 2010 (“ W IDC orldw IT Spending 2006– ide 2010 Forecast: The Worldw Black Book, Version 1,” ide 2006) “Integrating non-m ission-critical data warehouses w m ith ission-critical system creates an unm s anaged point of failure” Tim to GrowUp: (“ e The M odern, M ission-Critical Data Warehouse,” Gartner, March 2007) 7 July 1, 2009
  • 8. A newapproach to data w arehousing Em powering decision-making and action taking 8 July 1, 2009
  • 9. Business intelligence is evolving to becom an integral part of business e operations • Strategic • Operational • Reporting • Autom ating action • Standalone • M ission-critical com ponent • Weekly batch updates • Continuous online updates • Sim ETL ple • Sophisticated data integration • Single-function departm ental • EDW supporting “ single version of data m arts the truth” m for ultiple applications • Fewusers doing strategic • Thousands of users perform ing analysis m any types of tasks • Data volum < 1 TB e • Data volum at m than 100 TB es ore • Response tim and e • Near-real-tim response and 24x7 e availability not critical online everything • Sum arized data m • Detail plus years of history 9 July 1, 2009
  • 10. Business technology portfolio Technology for better business outcomes Provide good inform ation to Business Information Optimization produce better business decisions Business Technology Optimization Low risk to the enterprise er w better control of the ith infrastructure Adaptive infrastructure Reduce the cost of IT while Servers and Services Software delivering m to the ore storage business 10 July 1, 2009
  • 11. The HP Neoview platform answ the ers call Designed for the next generation 11 July 1, 2009
  • 12. W is the Neoviewplatform hat ? • An integrated hardw and softw platform for EDW designed to are are support terabytes of data and up to 256 processors • A com plete, preconfigured solution that can be rapidly deployed, easily m anaged, and is com patible w existing BI applications and system ith s • Built from redeployable standards-based hardw are com ponents ETL tools Query tools Standard Load/unload Integrated interface software hardw are softw are OS IBM W ebSphere Information Integration DBM S Managem console ent 12 July 1, 2009
  • 13. HP Neoviewplatform The next-generation enterprise data warehouse Simplicity and lower Enterprise-class TCO + capabilities ● Com odity platform m ● High-perform ance, ● Prebalanced, m assively parallel preconfigured, and database pretested ● Handles com plex queries ● Easy to incorporate into and m ultiple users existing environm ents ● Readily scales to 100s ● Rem otely m onitored and of processors m anaged by HP ● Built-in fault tolerance ● Reduced operating and adm inistration overhead Data warehouse platform Surrounded by world-class HP services 13 July 1, 2009
  • 14. Industry-standard components offer better value BI client Gigabit Ethernet …. ETL clients …. Sw itch fabric HP HP Integrity HP ServerNet StorageW orks servers technology Fibre Channel disks 14 July 1, 2009
  • 15. The Neoviewarchitecture is enhanced for decision support • Shared-nothing MPP ● Each processor is a unit of parallel work BI client ● Transparent softw virtualization are • Database virtualization ● Data is transparently hashed across all disks ● Balances I/O activity and processor utilization • Parallel query execution ● Queries are divided into subtasks and executed in parallel with results stream through m ory ed em ● Execution is pushed dow to low softw level n est are Fault tolerant ETL clients • ● Platform is available 24x7 in spite of any single point of hardw or softw failure are are ● Leverages 30 years of HP NonStop engineering • Extrem processing pow e er ● 1 Intel® Itanium 2 processor to tw pairs of RAID 1 drives ® o 15 July 1, 2009
  • 16. Neoviewplatform is designed to meet next-generation needs Architectural feature Key custom benefit er M assively parallel processing across hundreds Consistent high performance in mixed workloads of processors Shared-nothing architecture Linear scalability without bottlenecks or limits Advanced parallel query optimizer Fast processing of complex queries Built-in fault tolerance Superior availability without extra cost or managem overhead ent Rem m ote anagem and m ent onitoring from HP Simplified administration and reduced risk Industry-standard components Investm protection and easier data center ent integration High ratio of processing pow to storage er Reduced need for indexes and sum ary tables m Com pletely integrated hardware, software, Faster tim to benefit e and services 16 July 1, 2009
  • 17. “ tests processing tens of terabytes In of data in parallel across as m any as 256 processors, the HP Neoview platform delivers im pressive perform ance at a surprisingly attractive price… .” Richard Winter President, WinterCorp 17 July 1, 2009