Prevent Big Data Overload Before Your R12 . -
Prevent Big Data Overload BeforeYour R12 UpgradeOAUG Vendor Awareness SessionAprameya Bhat (A.B)ILM Architect and Practice Head1Julie LocknerVP, ILM Business Unit
Agenda The Impact of an Oracle E-Business Suite R12Upgrade on Data Volumes Lean Data Management Best Practices Informatica Data Archive Case Studies2
The Impact of an OEBS R12 Upgradeon Data Volumes3
R12 Upgrade Commentary End of Extended Support for 11i/10 November,2013 R12 Hardware Requirements - baseline Disk Minimum 20% Growth CPU Add 20% to 11i Memory Add 20% to 11i4
R12 Generates More DataOEBS 12OEBS 11iPRODOEBS UpgradeEnvironmentsPRODTEST /DEVTEST /DEVBACKUP/ DRBACKUP/ DRToo much data: Slows performance Lengthens test/devcycles Increases backup /recovery windowsPRODTEST /DEVBACKUP /DR5
One Example Of R12 Accelerated Data GrowthNew Modules I.e. Financial Services Accounting Hub(FSAH) – Now Called Financial Accounting Hub (FAH)OEBS R12 Users can efficiently create detailed,auditable, reconcilable accounting recordsfrom a variety of source systems.xla ae headersxla ae linesxla distribution links This module serves as one enterprise-wideaccounting repository to meet variouscorporate, management and reportingrequirements.With new functionality comes new tables and lots of data 6
Functional Specification – XLA - FAHSubledger Accounting (XLA) is a Rule-based accounting engine, toolset &repository supporting most of Oracle business Suite modulesIt is an intermediate step between Subledger products and the Oracle GeneralLedgerFAH efficiently creates detailed, auditable, reconcilable accounting for externalor legacy source systems working with the subledger accounting engine7
Exponential data generation in XLA-FAHmodelAll Subledger accounting data is generated and stored in shared SLA tablesprior to transfer to GL, this requires extensive population of SLA tables.Each Subledger transaction that requires accounting is represented by acomplete and balanced Subledger journal entry stored in a common datamodel called as event model that has event entity, event class and eventtype as transactional entities.Hence for one transaction enormous data is generated at XLA level.FAH includes an accounting transformation engine with extensive validationsplus accounting and rules repositoriesThe transformation engine consistently enforces accounting policies; therepositories provide centralized control, detailed audit trails, and facilitatesimultaneously meeting diverse corporate, management and reportingrequirements.As a result, between XLA and FAH, there is an uncontrollable andunmanageable data that is generated during implementations.8
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“What are your top data management challenges?”Top 3 ResponsesAll ChallengesManaging data growth& database sizeKeeping up with databaseperformance requirementsPrimary Challenge51%15%52%15%Maintaining security /compliance51%19%Source: ESG Research Report, 2011 Data Management, Survey10
“How does data growth impact your organization?”Top 3 Responses57%Increase in storage capacityrequirements42%Performance degradation37%Increasing cost ofinfrastructureSource: ESG Research Report, 2011 Data Management, Survey11
“What is causing database performance issues?”Top 3 ResponsesIncrease in transactionalworkload50%41%Storage I/O BottleneckToo much data36%Source: ESG Research Report, 2011 Data Management, Survey12
The Petabyte Challenge:2011 IOUG DATABASE GROWTH SURVEY13
Options To Address R12 Upgrade Issues Upgrade to Oracle Exadata Leverage ColumnarCompression Archive & Purge Lean Data Management14
Best Practices withLean Data Management15
Lean Data SInactiveInactive DataActive DataActiveTIME16
Lean Applications & Lean TestingInformatica Data Archive Purge data that isnever usedNeverUsed Partition & Archive datathat needs to be retainedProductionInactiveRDBMSActive17
Lean Data Management BenefitsOLTPEDWPRODPRODTEST / DEVBACKUP / DR Reduce costsImprove performanceStreamline operationsMaintain compliance18
Informatica Data Archive19
Informatica Application ILM SolutionsAddress Top R12 Upgrade ChallengesKey Benefits Increase performance Reduce storage, RDBMS license, personnel costs Reduce effort spent on maintenance & compliance Enforce retention and disposition policiesDATABASE SIZEProductionDevelopment/Testing/Training CopiesPerformanceCopy 1Copy 2Copy 3Copy 1Copy 2Copy 3Inactive dataActive dataTIME20
Data Archive Solution ArchitectureAnalyze & Act Based on Data Usage PatternsDiscoverOptimizeArchiveRetire Analyze and monitor datagrowth areas that impactperformance Classify Active vs Inactive Data Develop an effective strategy &act swiftly to rectify21
Identify Dormant Data for Oracle E-Business Suitewith Pre-built Accelerators & Free AnalysisDetermine Partition, Archive, &Purge Eligibility22
Informatica Data Archive Standard veRestoreSmart PartitionsLive ArchiveCentral Rules DefinitionAnd Management ConsoleInformatica Data Archive Standard Edition23
Informatica Smart PartitioningOptimize o purge Partition by time, geography, business unit, and/oractive/inactive, status etc. Data is physically stored as relationally intactsegments within the native database storage layer Partitions can be independently accessed andmanaged based on policy (i.e. compressed, archivedpurged, subset, etc.)Benefits Significantly improves application performance Automates complex database partitioning strategies based on business entities Simplifies future archiving as part of a comprehensive ILM strategy Streamlines cloning for test/dev by only copying relevant partitions No changes to how users access data24
Informatica Live ArchiveOptimize ResourcesStandard EditionProductionLive ArchiveArchive Aligns archive jobs with application entities Data is archived completely out ofproduction to either an online archive Maintains access to archive data eitherthrough native application UI or via standardODBJ/JDBC connectivityBenefits Application performance is significantly improved ensuring SLAs can be met End users access archive data via native application user interfaces Infrastructure costs and maintenance windows are drastically reduced Policies are centrally managed streamlining and improving compliance to retention policies25
Retain Archive Access For Business Users Uses Responsibilities to enable accessNo modifications to application codeNo additional application serverNo new user IDsNo user re-training26
Informatica Data Archive Advanced oreSmart PartitionsLive ArchiveStandard EditionRetireCompressNear-line ArchiveAdvanced EditionInformatica Data Archive Advanced Edition27
Maintain Zero Data GrowthInformatica Data ArchiveBEFORE SOLUTIONAFTER SOLUTIONGrowing storage costsPredictable manageable growthDiminishing performanceImproved, stable performanceIncreasing maintenance &Compliance workReduced maintenance &compliance workInformatica Confidential. For Discussion Purposes. Do Not Distribute.28
Lean Applications: Large TechnologyManufacturerSignificantly Improves R12 Performance Post UpgradeThe Challenge. The R12 upgrade generated 25% more data post upgrade, impactingfinancial reporting performance and leaving the infrastructure team unprepared withaccelerated storage consumption.The ResultAfterBeforeMaintain near zero datagrowthImproved app performanceto better than dictable resourceconsumptionReduced cloning times andresource requirements fornon-prod29
Lean Applications:IKON Office SolutionsImproves Customer Support Responsiveness& Saves 1.5 MThe Challenge. Response times for the customer support application was impacted bydata volumes accumulating in Oracle E-Business Suite. Additionally, full copies ofproduction were used for test/dev unnecessarily.The ResultAfterBeforeReduced storage costsby 1.5MProductionArchiveTest/DevSaved 14TB capacityusing lean datamanagement practicesSaved 4.8TB byarchivingReduced backupwindow by 25%ROI in 6 months30
You Can’t Manage What You Can’t MeasureTake Advantage of 3 Free Offerings1. Lean Data ManagementBusiness Value Assessment2. Application DataGrowth Analysis3. Lean Data WarehouseHealth Check31
Next Steps and Q&AContactVisit our website for more informationCaroline Athertoncatherton@informatica.comInformatica Solutions for ion ILM and Data Privacy Solutionswww.informatica.com solutions application ILMHighlighted AssetUpcoming EventsWhite PaperManaging Data Growth inOracle E-Business Suite withEnterprise Application Archiving,Test Data Management andData MaskingOracle Open WorldSeptember 30-October 4, 2012San Francisco, Moscone CenterBooth 2115, South Hall & Oracle Solaris PartnerPavilionUK Oracle User Group ConferenceDecember 3-5, 2012 – ICC, Birmingham, UKDOAG Conference ExhibitionNovember 20-22, 2012 – Nuremberg, NurembergConvention Center East (NCC)32
Thank-you!33
Informatica Application ILM Solutions Address Top R12 Upgrade Challenges . No user re-training Retain Archive Access For Business Users . 27 Standard Edition Advanced Edition Smart Partitions Live Archive Near-line Archive Informatica Data Archive Advanced Edition
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The Rise of Big Data Options 25 Beyond Hadoop 27 With Choice Come Decisions 28 ftoc 23 October 2012; 12:36:54 v. . Gauging Success 35 Chapter 5 Big Data Sources.37 Hunting for Data 38 Setting the Goal 39 Big Data Sources Growing 40 Diving Deeper into Big Data Sources 42 A Wealth of Public Information 43 Getting Started with Big Data .
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of big data and we discuss various aspect of big data. We define big data and discuss the parameters along which big data is defined. This includes the three v’s of big data which are velocity, volume and variety. Keywords— Big data, pet byte, Exabyte
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