exploiting operational intelligence within database ... · exploiting operational intelligence...

World®’16

ExploitingOperationalIntelligenceWithinDatabaseManagementToolsRonColmone,Sr ConsultingArchitectArunVijayaraghavan,Sr PrincipalProductManagerCATechnologies

MAINFRAMEANDWORKLOADAUTOMATION

MFX76E

2 ©2016CA.ALLRIGHTSRESERVED.@CAWORLD#CAWORLD

Abstract

Attendthissessiontogetaclose-uplookatcurrentlyembeddedoperationalintelligenceindatabasemanagementutilities.Discoverhowtoexploittheseavailablecapabilitiesaswellasthepossibilitiesforyoutobuildyouown!Youwilllearnhowadvancedautomationrequiresuniqueoperationalintelligencethatcanfurtherevaluatewhichadditionaltaskscanbeautomatedandtowhatdegree.Additionally,learnhowtosimplifyyourdaytodaydecisionmakingtominimizethetimerequiredtoeffectivelymanageandrunanefficientdatabasesystem.Theeffectsofallthis?HowabouthelpingtolowerDB2operatingcostsandimproveuserproductivity,includingmanualeffortandspecifically“specializedorexpert”manualrequirements.

RonColmone

ArunVijayaraghavan

Sr.ConsultingArchitectCATechnologies

Sr.PrincipalProductManagerCATechnologies


Agenda

USINGDATABASEANALYZERFORAUTOMATINGOBJECTMAINTENANCE

IDENTIFYINGSQLTOANALYZEFORIMPROVINGAPPLICATIONPERFORMANCE

IMPROVINGQUERYPERFORMANCEWITHPLANANALYZER

IDAAACCELERATIONWITHRC/QUERYANDSYSVIEW FORDB2

FUTUREOFOPERATIONALINTELLIGENCEINDB2

1

2

3

4

5


CADatabaseManagementPortfolioVisionManagingGrowthandComplexitytoAddressEvolvingDataManagementNeeds

Planned Available

IMS DB2

CAAdministrationSuite

CAPerformanceSuite

CAUtilitiesSuite

CARecoverySuite

ExperiencedDBA

WebAppServer

MobileDeveloper

BigDataAnalyst

RESTAPI

NextGenDBA

CAAnalyticsfor

DB2

NextGenDBA

ExternalApps


DatabaseAnalyzerProvidesOperationalIntelligenceforDB2ObjectMaintenance


DatabaseAnalyzer– PastandPresent

STATISTICSEVOLUTIONINDB2

QUICKOVERVIEWOFDATABASEANALYZERPROCESSING

TRADITIONALSTATISTICSCOLLECTIONANDANALYSIS

OBJECTMAINTENANCEUSINGDB2REALTIMESTATISTICS(RTS)

PRIORITIZEDOBJECTMAINTENANCE

1

2

3

4

5


Statisticsevolution– let’sstepbackandremember

§ RUNSTATSSinceDay-1– Collectedstatisticsfortheoptimizer– CollectedstatisticsfortheDBAtodeterminewhenREORGneeded

§ ExtentsaloteasiertotriggerthanCLR,LEAFDIST,FAROFF,NEAROFFetc.

§ Someissues/problemswiththisapproach– DB2cataloggotupdatedwithpotentially“bad”statistics– BIND’sandREBIND’sshouldbepostponeduntilstatisticsfixedby

REORGandanotherRUNSTATS



§ REPORTYESandUPDATENONE– Greatimprovementtoverifyobjectshealthcondition– NoneedtoworryaboutBIND,REBIND,dynamicSQLdueto“bad”

statistics– HadtochangeapplicationstointerpretRUNSTATSSYSOUTasopposed

tousingSQLagainsttheDB2catalogdeterminingREORGetc.

§ Objectskeptgrowinginnumberandsize– RUNSTATSexpensive– Samplingintroducedlateinthegame– Inlinestatscanbeusedwhenloading/reorganizingobject- Limited



§ RTSintroducedinDB2V7– fromDB29mandatory– Newchallengesintroduced(morelater)

§ Manywaystotriggerthereorgofanobject?– Performancemetrics– whenapplicationperformancestarttodegrade– Fixedschedule– Weekly,Monthly,etc.basedonobjectsize/type– Partitionedobjects- EntireTableSpaceoraPartitionatatime?

§ RTSiscommonlyusedtodayforObjectMaintenance

§ ToolslikeDatabaseAnalyzerorDB2’sDSNACCOXSP


RTSDrivenREORGsandSpaceAdministration

§ Somelimitationstothinkaboutbasedonrequirements– Istheobject(orpartition)over- orunderallocated?– DoyouwanttoalterQTYbasedoncurrentconditions/metrics?– CanyousaveanINDEX-LEVELbydoingareorg

(oneRTScolumnmighthelp)– Doyouneedtoprovideaccuratespacetrending/forecasting– DoyouhaverequirementstogenerateyourownSQLbasedconditions

todrivetheprocess– DoyouhaveaneedforIF-ELSElogicdrivingtheprocessesor

dynamicallydecidehowtheutilityshouldbegenerated– Slidingscalealgorithmresolvesmanyspaceallocationconcerns


DatabaseAnalyzerProcessOverview

DatabaseAnalyzerExtractandActionProcessing

DB2UtilityJOBs

DatabaseStatisticsExtract

ActionsConditions


DatabaseAnalyzer– Traditional(OldStyle)Space/Reorg

§ VerycommontobuildanExtractProcedureononeormoreDatabases(couldprocessentiresubsystem)

§ ActionProcedurescanbecreatedorselectedfromlistofbestpracticeDB2maintenanceoptions(Reorg,RUNSTATS,ImageCopy,etc.)

§ ProblemwithTraditionalDatabaseAnalyzerAutomation– Toomanyobjectstoprocess,onlysubsetmayneedattention– CPUintensivecollectingallthestatsneededtodoReorgorAlter– RTShasbeenavailableforsometimeandcanprovidenecessarystats


DatabaseAnalyzer– PreferredMethodisRTOS

§ RealTimeObjectSelection(RTOS)usesDB2RTS– NewwithR19- ObjectSelectionProfile(OSP)linkedtoExtractprocedure

tofilterobjectstobeprocessed(DatabaseandTablespacemasks)– CangenerateactionJCLforselectedUtilityprocessingbasedonRTS

conditions(Bestpracticearevaluesavailable)– OrcangenerateDatabaseAnalyzerStatisticsonthesubsetofobjectsand

continuetotakeadvantageoftheextensive“traditional”actionconditions– NewwithR19– PrioritizedObjectMaintenance(POM)profilescanbe

linkedtoExtractProcedurestoenabletheActionJCLgeneratedtobeprioritizedbasedonPrioritizedObjectConditions(POC)


OperationIntelligencewithDetectorSQLCollection


Detector– SQLCollectionforAcceleratedPerformance

§ AnalyticsAcceleratorUsageScenarios– Rapidlyacceleratebusinesscriticalqueries

§ OftenchallengingtotunequeriesthatalsosupportOLTPworkload– Improveaccesstohistoricaldata– Newbusinessintelligenceusecases

§ UseCADetectortohelpidentifylongrunningqueries– SortQueriesbyHighIN-DB2time– IdentifyDB2Tablesassociatedwiththeselongrunningqueries

§ IfrunningSubsystemAnalyzer,OptionTinDetectorwillshowtablesassociatedwithQuery


PlanAnalyzertoDeterminePotentialAcceleratedPerformanceImprovements


PlanAnalyzer- ACCELProfileSupport- ProfileServices

§ CreateanduseACCELprofilesusingtheDB2ProfileServicesfacility.ThefacilityusestheseDB2tables:– SYSIBM.DSN_PROFILE_TABLE– SYSIBM.DSN_PROFILE_HISTORY– SYSIBM.DSN_PROFILE_ATTRIBUTES– SYSIBM.DSN_PROFILE_ATTRIBUTES_HISTORY

§ ACCELprofilecostingisonlyappliedwhenDSNZPARMQUERY_ACCELERATIONisoneoftheseoptions:– ENABLE– ENABLEWITHFALLBACK


PlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport

ThresholdvaluesdetailstobeusedwhenAcceleratoraccessisconsidered

19.0 ---------- PPA DB2 Profile Services Create --------- 2016/04/05 03:32COMMAND ===>

PROFILEID ===> 5000 FUNCTION ===> ACCEL ENABLED ===> Y REMARKS ===> AIG DEMO >----------------------------------------------------------------- RASST02

ACCELERATOR THRESHOLDS: TABLE CARDINALITY ===> 2000000 N/A | -1 | 1 thru 2,147,483,647 RESULT SIZE ===> 1 N/A | -1 | 1 thru 2,147,483,647 TOTAL COST ===> +.0E+00 N/A | -1 | 0 thru +7.2E+75



STARTPROFoncommandlinewillactivatenewDB2Profile

19.0 -------------- PPA DB2 Command Display ------------- 2016/04/05 03:36COMMAND ===> SCROLL ===> PAGE

LINE 1 OF 24DSNT741I !DB2G DSNT1SDV START PROFILE IS COMPLETED. DSN9022I !DB2G DSNT1STR 'START PROFILE' NORMAL COMPLETION

Profile Services Status Report

Profile ID: 5000 Function: ACCEL Status TS: 2016-04-05-03.36.11.397468 Status : ACCEPTED BY DB2G

Attribute : ACCEL_RESULTSIZE_THRESHOLD Value : 1 Status : ACCEPTED

Attribute : ACCEL_TABLE_THRESHOLD Value : 2,000,000 Status : ACCEPTED

Attribute : ACCEL_TOTALCOST_THRESHOLD Value : 0 Status : ACCEPTED

Profile ID: 8661 Function: ACCEL Status TS: 2016-04-05-03.36.11.397468 Status : REJECTED BY DB2G - DUPLICATED SCOPE SPECIFIED

ID=8661wasalreadyactive.

TogglecommandonProfileServicespanelcanbeusedtoenable/disable



§ PossiblevaluesforQUERYACCELERATIONareobtainedfromSYSACCELERATEDPACKAGESandassignedtothespecialregister– (ENABLEWITHFAILBACK)BehaveslikeENABLE,butifanerroroccursonthefirstOPENof

theacceleratedstaticqueryDB2preventsthequeryfromfailing.Instead,DB2performsatemporarystatement-levelincrementalbindofthequery,andthequeryexecutesinDB2.

– (ELIGIBLE)Indicatesthatstaticqueriesareboundforacceleration,butareacceleratedonlywhentheymeetallaccelerationcriteriawiththeexceptionofcostandheuristicscriteria.QueriesthatdonotmeetthiscriteriaareboundforexecutioninDB2.

– (ALL)Indicatesthatallstaticqueriesareboundforacceleration,androutedtotheDB2Accelerator.IfDB2determinesaquerycannotbeacceleratedandthequeryreferencesauserbasetableorview,thebindorrebindpackagefailswithanerror.

SupportfornewBIND/REBINDcardsforpackages


CAPlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport

§ EXPLAINwithQUERYACCELERATION– Generatetheaccesspathinformationforpackagesthatwerebound

withthisoption,withoutissuingaREBIND.– FUTUREexplainwillSETthespecialregisterCURRENTQUERY

ACCELERATIONbeforethedynamicexplaintoreflectthevalueboundinthecatalogforthepackage.

– IfDSNZPARMACCELMODEL=YES,FutureExplainoverridesthevaluethatisspecifiedintheQUERYACCELERATIONbindoptiontoNONE.Thisactionallowstheaccesspathtogeneratesuccessfully.(nextslidewillillustrate)


CAPlanAnalyzer:IBMDB2AnalyticsAcceleratorsupport

ACCESStypeillustratesthequerycanbeexecutedontheIBMDB2Analytics

Accelerator

ViewDSN_QUERYINFO_TABLEwhenusingExplainProfile– PLAN_TABLEOption===>ALL


AccelerateandMonitorIDAATableswithRC/Query,BatchProcessorandSysview forDB2


CADatabaseAdministrationforDB2forz/OS

§ CARC/Query &BatchProcessor(canusewithautomation)– Noneedtonavigatebetweenmultiplesolutions– ManageDB2AnalyticsAcceleratorwithinyourCADB2solutions

§ Display/Start/StopAccelerator§ Add/RemovetablestoDB2AnalyticsAccelerator§ Loadtabletotheaccelerator§ Enable/Disabletableacceleration/replication§ AlterAcceleratedtableDist.&OrgKeys§ ArchiveAcceleratedtable§ ForceRemoveTablefromDB2AnalyticsAccelerator§ Restorethearchivedmovedpartitionsdata

NewwithR19


ManageDB2AnalyticsAcceleratorUseCARC/QueryforIDAAadministration

Use“?”linecommandtoseeavailablecommands.

IDAATablelevelfunctionsareavailabletoDefine,Alter,Remove,Load,etc.


ManageDB2AnalyticsAccelerator

§ LoadDB2TableintoIDAA– RC/QALOADCommand

– CanloadrangeofpartitionsorentireTable

– UsesIDAALoaderutility

– MostcommandssuchasALOADareavailableusingBatchProcessorScripts

UseCARC/QueryforIDAAadministration


SYSVIEWforDB2– IDAASubsystemStatistics

§ Newacceleratorrelatedfieldswereaddedtorecords:– Databasestatistics(IFCIDs2and1002)– DB2systemparameters(IFCIDs106and1006)

§ Updatedreports:– SYSACCEL/SYSACDTL:AcceleratorList/Detail– HSACCLST/SYSCCDTL:AcceleratorServerList/Detail(history)– HSUACLST/HSUACDTL:AcceleratorServerList/DetailSummary(history)– BTSTATR1:StatisticsDataTrace(batch)– BTSTASM2:SummaryofDB2databaseaddressspacestatistics(batch)– SYSSTATS/SYSTATA,GRPSTATS/GRPSTATA:SystemOverview


DB2AnalyticsAccelerator

§ Subsystemstatistics:AcceleratorDetails– SYSACDTL:

AcceleratorDetails– HSACCDTL/

HSUACDTL:AcceleratorServerDetails/Summary

CASYSVIEW®PerformanceManagementforDB2


DataDrivenDBPerformanceManagementMainframeOperationalIntelligence


EXPERTSYSTEMS&MACHINELEARNING§ AnalysisofHistorical,Near

Real-timeplusLivedata§ DataMining,MachineLearning

&Autonomics§ SelfDrivingCars

DATAMINING&DECISIONSUPPORT§ AnalysisofHistorical&NearReal-timeData§ DataMining,Aggregations&Decision

Support§ NavigationToolswithTrafficUpdates

BI&STATISTICALANALYSIS§ AnalysisofHistoricalData§ BIReporting,Slicing/Dicing§ BasicNavigationTools

EvolutionofOperationalIntelligenceActionableanalyticswithsignificantadvancestowardszerotouchoperations

ANALYTICS1.0BUSINESSINTELLIGENCE

ANALYTICS3.0DATA-DRIVENAPPLICATIONS

ANALYTICS2.0BIGDATA

TechnologyTrend


55%ofapps

dependonmainframe

70%world’scorporate

dataisonamainframe

70%oftransactionsflowthroughmainframe

64%Increaseinmainframeworkloads

ANOMALYDETECTION

BUSINESSSERVICEMANAGEMENT

EXPERTSYSTEMS

SECURITYBREACH

DETECTION

IndustryOpportunityDataAnalyticsandtheMainframeOpportunity


MainframeOperationalIntelligenceLeveragetechnologytrendtosolveanimportantMainframeproblem

ANOMALYDETECTION

REPORTINGMODERNIZATION

BUSINESSSERVICEPERSPECTIVES

EXPERTSYSTEMS

• Reacttothresholdevents

• Selfserviceviews• ModernU/X

• Predictanomalies• Proactiveresponse

• Topologydiscovery• Businessserviceperspectives

• Predictbusinessservicedisruption

• Rootcausehypothesis• Resolution guidance• Automateresponse

DataAnalyticsBI/StatisticalModeling

USERS

Generalist

MAINFRAM

EOPERA

TIONS

Experts

Specialists

Automation

ENTERP

RISE

SUPP

ORT

MTTR&FirefightingOptimizedPerformance&Efficiency

AutomationDataDrivenOperations


IntelligentMainframeOperations- LiftingtheBurden

DATA-DRIVENAPPLICATIONS

ANALYTICALTOOLS&SKILLS

IN-HOUSEDATASCIENCE

IN-HOUSEDOMAINEXPERTISE

+

+

YOURBURDEN MainframeTeamCenter

LettheDATAdotheWORKforyou

DoMOREwithLESS!

Fueledby:§ advancedanalytic

algorithms§ machinelearning


MainframeOperationalIntelligenceKeycapabilitiesbeingdevelopedtomanagethelifecycleofyourdataassets

AnalyticsProcessing Multi-ChannelDelivery

Multi-ChannelDataCapture

DistributedDB2,Oracle,MySQL,

Cassandra...

MainframeVSAM,DB2,IMSDB,IDMS,DATACOM,

SMF,Syslogs,Vtape,CICS

Itsyourdata,leverageittodotheworkforyou!

DataLifecycleManagement

• ReportDistribution• AnomalyAlerts• Ad-HocAnalysisTools• APIbaseddataaccess• Automationframeworks

ExpertSystemsAutonomics

Transport&Integration ScalableData

Storage

DataScienceandAnomalyDetection

BusinessIntelligence

ITOperationsIntelligence

APIManagement

MasterDataManagement


DataDrivenDBPerformanceManagementProblem,OpportunityandProposedSolution


INCREASINGCOMPLEXITY REACTIVETOPROACTIVE EARLYWARNING

CompaniesaredevelopingcomplexSQL applicationsonDB2.Performanceoftendegradesovertimewithoutanyonenoticing(thecreepingtrend).

AnintelligentsystemthatcanrecognizeandprioritizesignificantchangesinSQLperformancebeforeitstartstoimpactresourceoverheadsand servicelevelagreements

ADBAoftendoesnotrecognizedegradationuntilcustomerscomplain orservicelevelagreementshavebeenmissed.

TheSituationEverincreasingapplicationvolumesandcomplexity


§ Howdoyoudeterminewhenperformancestartedtodegrade?§ HowlongdoesittaketoidentifytheproblematicSQLstatements?§ Doyouknowhowthestatementsexecutedbeforetheperformance

degraded?§ Doyouhavealogofapplicationperformanceproblemsand

resolutions?– Canyoucomparethislogwithcurrentproblems?

§ ManycustomersoffloadCADetectordatastoreintoDB2tables– Queryheaviestplans/packages&manuallycompareto“baselines”– Noefficientmethodtore-evaluatebaselineswhen“theworldchanges”

– Howtomonitor“standarddeviation”and“creepingtrend”iscomplexandcumbersome

TheChallengeHowdoyouresolveperformanceproblems?


Capture CADetectorcollectsSQL

intervalperformancemetricsforanalysis.Createsan historicalviewofapplicationperformance,whichwillbestoredinthecloud.

Baselin

e BaselinesarecreatedthatcontainastatisticalanalysisofapplicationSQLperformanceforachosen,representative,period-in-time.Baselinerepositorycanbeupdatedwhenbaselinesaredeterminedtonolongerberepresentative.

Analyze Analyzesapplication

performancedeviationsfromnormalexecutionbehavior(i.e.,deviationsfrombaselines)andlogsinaneventsrepository. N

otify

DuringtheAnalyzestep,notificationsofperformancedeviationscanbesenttokeypersonnelviaemail.

Review

InadditiontooutputfromCapture,Baseline,andAnalyzetheapplicationwillallowyoutoreviewapplicationperformancehistorythroughawebbrowserinterface.Deeperanalyticsandvisualizationareavailablethroughthebrowserdashboard.

Mainframe

TheSolutionMainframeTeamCenter–OperationalIntelligence


USECASE:Sherman,Debbie,andFredhavedeepexperienceandskillsintheirrespectiveareas.Theyhatebeingcalledafterasystemproblemhasoccurredandbeingaskedtoprovethattheirareawasatfaultorinnocent.Theywouldlikeasystemthattellsthemwhentheirareaisbehavingabnormally,sothattheycanaddressitbeforeitcomestotheattentionoftheSystemsPerformanceEngineer.

• Operations,SystemsProgrammer,• NetworkEngineer,ApplicationDBA

PERSONA:

PROBLEM§ “Avoid,detectandpredictissuesthatmightbeaproblem”§ “Thresholdsarehardtomaintainandgeneratelargeamountoffalsepositives”§ “Abilitytoseemultipleviewsofdata”§ “Abilitytocreateviewsofinformationfaster”§ “ThecurrentU/Xpreventseasycollaborationandaccesstoanalytics”

AnomalyDetection– ProblemAvoidance/Prediction


AnomalyDetection– ProblemAvoidance/PredictionSOLUTION§ Detectanomalies&predictissuesreal-time,alertbasedonpredefinedrules

§ Leveragehistoricaldataandmachinelearningfordynamicthresholds

§ SimplifiedU/X– browseraccess&designedforcollaboration

BOTTOMLINE§ HighAvailability

§ Problemavoidance§ ReducedMTTR

§ ReduceSMEdependenceforissuedetection


AnomalyDetection– DeepDive

§ UtilizeHistoricaldata§ DefinebandsofLikelyandunlikelyvalues

§ Maprealtimemetricstreamsagainstsystemdefinednormal

§ Multi-pointalertsgeneratedusingindustry-standardWestern-Electricrules

§ Makestaticthresholdsoptional!

Letdatadotheworkforyou

Unlikely

MostLikely

Metric

Time

TypicalVolatility

Anomaly

Tasksready

tobedispatched

LessLikely


USE CASE:As someone owning IT operations and responsible for technology progress, my MF systems should exploitplatforms like Linux (X86 & Z) to offload data processing and non essential management capabilities. Lack of thiscapability limits the ability to extend the strengths of the mainframe with next generation technology that issupported on Linux.

• BusinessOwner• TechnologyOwner

PERSONA:

PROBLEM§ “Helpmedeployyouranalyticssolutioninhoursnotdays”§ “Nosignificantincreaseonmyteamsworkload,wearenotlinux experts”§ “WeplantoexpandourzecosystemtoincludeLinuxandrequiretoolstoleverageourstrategy”§ “Ensuringtechnologyprogresswithoutlosingthereliability/securityofMFishighvaluegoal”§ ”Ensureanalyticssolutiondonotincreasemyoperationscostssignificantly”

SimplifiedDeployment– MinimizeCostandRisk


SimplifiedDeployment– MinimizeCostandRisk

Web-basedUI

RestAPI

z/OS

PerformanceManagement

DatabaseManagement

RestAPIs

StorageManagement

SoftwareVirtual

Appliance

BENEFITS§ LowCostcomputing§ Non-Invasive§ RetainsSecurity§ SetupinMinutes

ANALYTICSSTREAM

EXISTINGPRODUCTS

Browser

SOLUTION§ Virtualappliance§ ContainerizedPlug-n-play§ SimpleConfig GUI

MetricBaseline

AnomalyDetection

Correlations

Aggregation

HistoricalData


BusinessServicePerspectives&TopologyViewsSOLUTION§ Monitorservicelevelmetrics

§ Analyzemetricswithabilitytodrilldownacrossentiretopology

§ Trackandprioritizeeventsaffectingservices,consolidatealerts

§ Correlatemetricsviamachinelearning

§ QuickrootcauseanalysisandreducedMTTR

BOTTOMLINE§ ReducedMTTR

§ ReduceSMEdependenceforissuetriage/investigation

§ Improvedenduserexperience


ExpertSystems– IntelligentMainframe

SME

•SOLUTION§ AssistedtriagethathelpsfasterRCA,reduced

MTTRandfacilitatesbetterdecisions§ Utilizecorrelationdatasciencetofindclusters

ofalertsandrelationships

§ Leveragemachinelearning&yourdatatothesystemtriageandisolatetheproblem

§ Systemlearnsfromexperts,helpsonboardjuniorstafffaster

BENEFITS§ ReducedMTTR

§ Higherproductivityofjuniorstaff§ TransferofTribalknowledgetoanexpertsystem

§ Lowercostsofoperations,noincreaseinrisks

1

2

3

41 3

2

4


LEVERAGEDATAITSYOURASSET

MOVEFROMREACTIVETOPROACTIVE

DETECTANOMALIESAVOIDPROBLEMS

WrapUpandKeyTakeawayMainframeOperationalIntelligence&DB2Tools

Expertuserswithdecadesofexperience

Highlycustomizable,flexibletoolkits

Basicsoftwareintelligence

InexperiencedusersnewtoMainframe

Toolsthatguideuserstodecisions

Predictivesoftware

intelligence

WeInviteyoutoparticipateinDesign@CAandTechPreview!


RecommendedSessions

SESSION# TITLE DATE/TIME

MFX79E ProtectingandTappingIntoyourDataGoldmine:leveragingwhatresidesinyourmainframe 11/15/2016at9:00am

MFX80EIntelligentMainframeManagement- DataDrivenDatabase PerformanceManagement 11/15/2016at10:00am

MFX81EHowtoGettheMostoutofYourDB2,DB2Management,andAnalyticsInvestment 11/15/2016at11:00am

MFX88S StrategyandVisionforCADB2DatabaseManagement 11/17/2016at12:45pm

MFX90S DrivingDownCostsforDB2Management 11/17/2016at1:45pm

MFX91S BirdsofaFeather/StumptheTechieforCADB2Tools! 11/17/2016at3:00pm


MustSeeDemos

DemoNameServicesYTheater#location

DemoNameSolutionYTheater#location

DemoNameProductXTheater#location

DemoNameProductXTheater#location


Questions?


Thankyou.

Stayconnectedatcommunities.ca.com


©2016CA.Allrightsreserved.Alltrademarksreferencedhereinbelongtotheirrespectivecompanies.

Thecontentprovidedinthis CAWorld2016presentationisintendedforinformationalpurposesonlyanddoesnotformanytypeofwarranty. The informationprovidedbyaCApartnerand/orCAcustomerhasnotbeenreviewedforaccuracybyCA.

ForInformationalPurposesOnlyTermsofthisPresentation

exploiting operational intelligence within database ... · exploiting operational intelligence...

Documents