Why NoSQL databases are needed for the Internet of Things

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1 MachinaResearch//ResearchNote MachinaResearch,2014 MachinaResearch ResearchNote WhyNoSQLdatabasesareneededfor theinternetofthings 1 EmilBerthelsen,PrincipalAnalyst April2014 Theissue Ourview BigDataintheInternetofThingsissnowballing.Theamountofdataisever-increasing and becoming more and more varied. The impact on traditional relational database managementsystems(rdbms)willbesignificant.databasesneedtoadoptandmeet these new IoT requirements with greater data processing agility, multiple analytical tools including real-time analytics, and aligned and consistent views of the data. This Research Note examines what database capabilities will be required to address data managedintheinternetofthings,andhownosqlsystemslikemongodb,cassandra andhbasearemeetingthischallenge. Thetraditionalrelationaldatabasemanagementsystemswillcontinuetohavearole in the Internet of Things when processing structured, highly uniform data sets, generated from a vast number of enterprise IT systems and where this data is managed in a relatively isolated manner. When it comes to managing more heterogeneous data generated by millions and millions of sensors, devices and gateways, each with their own data structures and potentially becoming connected and integrated over the course of many years, databases will require new levels of flexibility, agility and scalability. In this environment, NoSQL databases are proving theirvalue. ThesignificanceoftheInternetofThingsisnotthatmoreandmoredevices,peopleandsystemsare connected with one another. It is that the data generated from these things is shared, processed, analysedandacteduponthrough newandinnovativeapplications,applying completelynew analysis methods and within significantly altered timeframes. The Internet of Things will drive Big Data, providingmoreinformation,frommanydifferentsources,inreal-time,andallowustogaincompletely newperspectivesontheenvironmentsaroundus. The difference between machine-to-machine (M2M) and the Internet of Things (IoT) is driven by significant changes in thehandling of data. In M2M, machine-generated data generally reflectswell- defineddatasets,communicatedwithinestablishedprotocolsandformats,anddeliverswell-defined alertsandnotificationswhenvaluesexceedtheirparameters.applicationsinm2mmakeefficientuse ofthisdataastheseapplicationshavebeendevelopedhand-in-handwithwhatthecharacteristicsof

2 MachinaResearch//ResearchNote MachinaResearch, thedata.ineffect,applicationanddataareintrinsicallydesignedasonetomeetthespecificpurposes oftheapplicationinfairlyrobustyetstaticmodel. IntheInternetofThings,theworldofdataandapplicationsrequiressignificantlymoreflexibility,agility andscalability.theserequirementsaredrivenbyalimitednumberofunderlyingneeds.thisresearch Noteexplorestheserequirementsandunderlyingneedsinmoredetail,including:! Diversity of devices and data Data generated from an exponentially growing number of diversesensors,devices,applications,andthingswillbeaccompaniedbyagrowingdiversityin thestructureandscaleofthatdata andmoreandmoresourcesofadditionaldata ranging fromdatasourcedfromcorporatesystemstocrowdsourceddatawillneedtobecombinedwith thisdata.! Flexibleandagilesystems AstheInternetofThingsbecomesamoreopensystemwherenew sensors,devices,applicationsandthingsareaddedandconnected,and evolve what weterm Subnets of Things, 1 thesupportingapplicationsanddatabasesthatmanagethedatagenerated from these things will need to remain flexible and agile, allowing businesses to meet their requirementswithouthavingtoreinventtheapplicationeverytime.! Sophisticatedanalytics Where simple alerts and notifications were the backbone of M2M systems,analyticsbecomesthecornerstoneoftheinternetofthings,requiringenhancedand multipleanalyticalapproachestoaddresstherequirementsoftheapplications. It should come as no surprise that developments in the Internet of Things and databases have run parallelwithdiscussionsaroundbigdata,atopicexploredinotherpublicationsbymachinaresearch. 2 TherelationshipbetweenIoTandBigData,andthechangesfromM2MtoIoTarecapturedinFigure1. Figure1:M2M,IoTandBigDataconnected[Source:MachinaResearch,2014] 1 MachinaResearchcreatedthe term Subnets of Things in contextofour BigDatainM2M:Tippingpointsand Subnets of Things White Paper released in February 2013, in referring to an island of interconnected devices, driven either by a single point of control, single point of data aggregation, or potentially a common cause or technologystandard 2 MachinaResearchhaspublishedaseriesofResearchNotes,aStrategyReportandaWhitePaperonthetopicof Big Data as this remains a significant opportunity area in M2M and IoT for operators, system integrators, and solutionandplatformproviders.

3 MachinaResearch//ResearchNote MachinaResearch, What is important to take notice of is that improvements in technologies as illustrated by M2M/IoT ApplicationPlatforms 3 andnosqldatabasesareenablingthesedevelopmentstotakeplace. 1 WhatisNoSQL? SQL stands for Structured Query Language, designed for managing data in relational database management systems (RDBMS). NoSQL, referred to as Not Only SQL,wasdesignedformanagingdata whichdidnotnecessarilyhavethestructureofrdbms.someoftheleadingplayersinrdbmsarewell- establishednamessuchasoracle,ibm,microsoftandsap. Instead of data being structured in fixed relational columns, data could be stored in objects of, for example,graphformat(neo4jandvirtuoso),keyvalueformat(riakanddynamodb)orwidecolumn formats (Accumulo, Cassandra and HBase). One additional and widely adopted format of NoSQL databaseisthedocument-basedsolutionaspresentedbymongodbandcouchdb. ToillustratethefundamentaldifferencebetweenRDBMSandadocument-basedNoSQLsolution,the formerstoresdatainhighlystructuredrelationaldatabaseswheredataschemesare fixed, andcurated datahastoconformtospecificcharacteristics(fieldlength,characters,etc.)defined.nosqldatabases, inanutshell,allowfordatatobestoredinwhatessentiallymaybedefinedasform-freeformats,i.e. withoutarigidityorstructureofrdbms,andallowingforalltypesofstructured,semi-structuredand completely unstructured data to be stored in an object. The significant benefit of this approach becomesclearwhenconsideringthevastamountsofdatageneratedby things which may exhibit an equally extensive range of structures and qualities and from which diverse data needs to be pulled togetherandanalysedinordertocreatetheiot.thisdiversitymayrangefromsmallerdifferencesin data structures (for example different field lengths or which data fields are captured) to more substantial differences such as data without any immediate structure as exhibited by images, audio files,randomcontentintextmessages,facebooklikes,andsoon.nosqlprovidessignificantflexibility withregardstodatamanagement. 2 Extendingdatamodelschemaandanalytical processing Another challenge presented by the Internet of Things involves the extensibility in the software. As businessesrecognizeandrealizenewopportunitiesfromtheexpandingestateofsensorsanddevices implemented and the data generated, additional requirements from applications and supporting 3 For more detailed information about M2M/IoT Application Platforms, see Machina Research s White Paper on TheEmergenceofM2M/IoTApplicationPlatforms published in September 2013

4 MachinaResearch//ResearchNote MachinaResearch, systems emerge. As seen with the development in M2M/IoT Application Platforms, 4 managing and developingapplicationsandaddressingscalabilityaresignificantandnewcapabilitiesforplatformsin the age of the Internet of Things. These developments will drive innovations in databases, data analytics,andthestructureofdatastorage. Responding to the Internet of Things with relational database management systems (RDBMS) is an optionbutpresentsalimitingfactorwhichintimewillbecomeasignificantobstacletorealizationofthe full opportunities available from all types of data. Databases in the Internet of Things require the flexibilityofthenosqlapproach,allowingasexploredearlier,differenttypesofdatatobestoredbut moreimportantly,theagilityandflexibilitytoadapttheunderlyingdatamodelstonewandchanging businessrequirementsandapplications. Applications and data models follow the requirements of businesses, and another key development areaneededwillbeindataanalytics.nolongerlimitedtohistoricaldataanalysis,dataanalyticsmoves to as close to real-time analytics as possible, introducing and requiring tools such as data streaming analysis, and with the growing numbers of multiple data sources, Complex Event Processing. In the Internet of Things, data analysis will require multiple analytical approaches, and in some cases, significantvalueisachievedfromreal-timedataanalytics(forexample,inmissioncriticalorlife-saving scenarios where information may guide rescue services within dangerous environments) or from historicalanalysisforpredictivemaintenanceservices. TheInternetofThingsgeneratessignificantlymoredatatobestored,andwhilecloudbasedservices havecertainlyprovidedanefficientandhighlyscalablesolution,developingtoolstomanagedistributed databases (rather than located on a single server) emerge asonemore capability required from the platforms.inthiscorner,hadoopwithitshdfsarchitectureallowsforhundredsandeventhousandsof nodestobecomepartofthedatacluster,anewanddistributedwayofmanagingdata. Finally,thelocationofdatastorageandstorageoptimizationwillbecomeimportantfactors.Thespeed and frequency with which data will need to be accessed by different applications may change, from instancetoinstance,theoptimalconfigurationofdatastorage.essentially,theidealdatabasestructure requiredtosupport(forexample)offlinehistoricalanalysesisdifferenttothatrequiredtosupportreal time analyses. However, specific datasets may be required to support either off line or real time applications(or,morerealistically,aneverevolvingmixofrealtimeandnon-realtimeapplicationsthat changesovertimeasnewiotapplicationsaredeveloped).theimplicationsthatanevolvingmixofreal timeandofflineapplicationsaccessingrelateddatasetsmayhaveonoptimaldatabasestructureisjust oneillustrationofawiderdynamic:everchangingapplicationrequirementsintheinternetofthingswill resultinaneedtodynamicallymanagetheoptimalstructureofassociateddatabasesonanongoing basis. 4 For more detailed information about M2M/IoT Application Platforms, see Machina Research s White Paper on TheEmergenceofM2M/IoTApplicationPlatforms published in September 2013

5 MachinaResearch//ResearchNote MachinaResearch, Dataintegrationbecomesthechallenge 5 Howtohandledatainterrogationisnotanewchallenge,butitisasignificantlygreateronegiventhe volumeofdatainvolved,itsdiversity,multiplestoragelocationsanddifferentanalyticalprocesses.the unifiedviewofdatabecomesthechallengethatdataanalyticsplatformswillneedtoaddress. This challenge involves, at a fundamental level, the development and application of semantics technologies,recognising,forexample,commonvocabulariesorlinkeddata.atmoreadvancedlevels, dataintegrationinvolvesmappingandovercomingtheheterogeneityofthedata,andpresentingthis withaunifiedview. Theadditionalchallengecomesfromunifyingthedataattwolevels,thefirstofwhichisclearlyinterms of the actual data produced by sensors and devices such as temperature readings, on/off status, vibration,etc. Atasecondlevel,andperhapssignificantwhenlookingtogroupsensorsanddevicesaccordingtoany numberofcriteriathatmaybechosen,thetaskthenbecomesoneofaugmentingdataasitarrivesin supporting platforms or the databases, and ensuring that these data identifiers (or metadata) are correctlyaggregatedandprocessedalongsideassociateddata,enhancingthecapabilityofplatformsin verifyingandcontextualisingdata. 4 Conclusionsandrecommendations Platformsmanagingdataandapplicationsrequiresignificantlymoreflexibility,agilityandscalabilityto meet the requirementsof businesses in the Internet of Things. These requirementswill be dynamic, constantly changing as the opportunities from implemented sensors and devices, and their data are identified. Businesses will look to gain and maintain competitive advantage from innovative applications,requiringquickerapplicationdevelopmentandagiledatamodels. In this environment of the Internet of Things and Big Data, Machina Research makes the following recommendations:! EnterprisesshouldcarefullyconsiderdifferenttypesofdatabasesasillustratedinFigure2to addressthedatageneratedfromtheexponentiallygrowingnumberofdiversesensors,devices, andthingsaswellasthegrowingdiversityinstructureandscaleofthatdata.insomeinstances, SQLandhybriddatabaseswillmeettherequirementsofbusinesses,andinothers,asaddressed inthisresearchnote,anosqldatabasewillbethewayforward.

6 MachinaResearch//ResearchNote MachinaResearch, Figure2:NewCapabilitiesintheInternetofThings[Source:MachinaResearch,2014]! Providersofdatabaseswillneedtoensurethattheirsolutionsareflexibleandagile,meeting thedynamicrequirementsofbusinesswithouthavingtoreinventtheapplicationordatamodel everytime,andhavingrobustintegrationswithm2m/iotapplicationplatforms! Adiverseapproachto dataanalyticswillpaydividends.forprovidersofdataanalytics, the abilitytosupportmultipleanalyticalapproachestoaddressthevariedrequirementsofbusiness applicationsanddatawillbecomeanincreasinglyimportantfeatureinthefuture.

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