4 Ways Hadoop & Spark Can Play Nicely Together – Business Intelligence Info

2019年12月8日 admin bckbet下载

Asorganizationsbuyintobigdata,ahugepartoftheprocessisselectingthetoolstostore,maintain,process,andanalyzethedata。

HadoopandSparkareoftenbilledasaneither-orscenario。

Eitheryouuseone,oryouusetheother。

However,therearereasonswhyyoushouldconsiderusingboth。

Inmanysituations,theycomplementeachotherbeautifully。

Hereiswhateachdoesandhowtheyaredifferent,butcanoftenbeusedtogetherascomplementarytools。

1。

HadoopincludesaDistributedStorageFramework,SparkProvidesIn-MemoryDataProcessing

Hadoopcanplaynicelyinapack,buttobeacompletebigdatasolution,itneedstoincludeitsnativedataprocessingcomponent,MapReduce,orbepairedwithanotherdataprocessingproductlikeSpark。

HadoopincludesHDFS,adistributedfileframework,whichallowsyoutodistributeenormouscollectionsofdataacrossnodeswithinaclusterofservers。

Thiseliminatestheneedforlotsofcustomhardware。

Hadoopalsoindexesandtracksthedata,whichallowsforprocessingandanalyzingthosemassivedatacollectionsmoreefficientlyandeffectively。

Sparkdoesnotdistributestorage,itonlyprocessesthedata。

Hence,bothHadoopandSparkcanworkeffectivelyasabigdatasystemthatcombinesarequireddistributedfilesystemwithSpark’smulti-stage,in-memorydataprocessing。

2。

ThoughHadoopSparkWorkWellTogether,ItIsn’tNecessarytoHaveBoth

InadditiontoHadoop’sstoragecomponent(which,bytheway,iscalledtheHadoopDistributedFileSystemorHDFS),itoffersMapReduceforprocessingpurposes。

ThiswouldeliminatethenecessityforSpark,andisusedthiswayinmanybigdatainfrastructures。

Similarly,SparkcanbeusedwithafilemanagementsystemotherthanHadoop。

ButsinceSparkwasdesignedtobeusedwithHadoop,thetwoaregreatcompanions。

Plus,MapReduceisknownforbeingdifficulttoprogramin。

Sparkissimplerandfaster。

3。

SparkisFasterThanMapReduce

Sparkisn’tessentialforHadoop,butifyouneedtoworkinrealtime,itisbetweentenand100timesfasterthanMapReduce。

Whenconsideringabigdatainfrastructure,ifspeedisaconsideration(suchaswhendatastreamingisrequired),SparkisfasterthanMapReduce。

Sparkcandelivernearreal-timeanalysisandSparklooksatallofthedata,whereasMapReducereadsdatafromonecluster,performsanoperation,thenwritestheresultsinasystematicmethodthatslowstheoperationsdownconsiderably。

Dependingonthesetup,Sparkoftenperforms100timesfasterthanMapReduce。

4。

BothHadoopSparkareResilienttoSystemFailures

Hadoopwritesdatatodiskfollowingeachoperation,makingitresilientwhenafaultorfailureoccursinthesystem。

Sparkalsohasaresilientdesign,itjustworksdifferently。

SparkstoresdataobjectsinresilientdistributeddatasetsorRDD,whicharedistributedacrosstheclusters。

Thedatamightbestoredinmemory,orstoredonthedisks。

RDDassuresfullrecoveryfollowingafaultorfailure。

Hence,ifyouareusingHadoopandSparkseparately,thereisstillabuilt-inresilience。

Together,however,thisduomakesforasoundinfrastructureforbigdataprocessingandanalytics。

Editor’sNote:Whetheryou’reworkingwithHadoopand/orSpark,yourfirstjobisgettingyourdatafromyourexistingdatainfrastructureintoHadoopinausableformat。

Thiscanbetrickierthanitsounds–especiallyifyourdatasourcesincludemainframes。

YoucanexploreSyncsort’sBigDatasolutionstoseehowtheirexpertiseinHadoop,Spark,mainframesanddatawarehouseoptimizationcanhelp。

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