Web-Scale Data Management for the Cloud

208 Pages·2013·4.278 MB·English

by  Wolfgang Lehner, Kai-Uwe Sattler (auth.)

Wolfgang Lehner
Kai-Uwe Sattler
Web-Scale Data
Management
for the Cloud
Web-Scale Data Management for the Cloud

Wolfgang Lehner • Kai-Uwe Sattler
Web-Scale Data
Management for the Cloud
123
WolfgangLehner Kai-UweSattler
DresdenUniversityofTechnology IlmenauUniversityofTechnology
Dresden,Germany Ilmenau,Germany
ISBN978-1-4614-6855-4 ISBN978-1-4614-6856-1(eBook)
DOI10.1007/978-1-4614-6856-1
SpringerNewYorkHeidelbergDordrechtLondon
LibraryofCongressControlNumber:2013932864
©SpringerScience+BusinessMediaNewYork2013
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,
broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology
nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection
with reviews or scholarly analysis or material supplied specifically for the purpose of being entered
and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of
this publication or parts thereof is permitted only under the provisions of the Copyright Law of the
Publisher’slocation,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.
PermissionsforusemaybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violations
areliabletoprosecutionundertherespectiveCopyrightLaw.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
While the advice and information in this book are believed to be true and accurate at the date of
publication,neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityfor
anyerrorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,with
respecttothematerialcontainedherein.
Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
Thisbookisdedicatedto ourfamilies–this
bookwould nothavebeen possiblewithout
theirconstantand comprehensivesupport.

Preface
The efficient management of data is a core asset of almost every organization.
Data is collected, stored, manipulated, and analysed to drive the business and
derive support for the decision making process. Establishing and running the
data management platform within a larger organization is a complex, time- and
budget-intensivetasks.Notonlydodatamanagementsystemshavetobeinstalled,
deployed,andpopulatedwithdifferentsetsofdata.Thedatamanagementplatform
ofanorganizationhastoconstantlymaintainedonatechnicalaswellasonacontent
level.Changingapplicationsandbusinessrequirementshavetobereflectedwithin
thetechnicalsetup,newsoftwareversionshavetobeinstalled,hardwarehastobe
exchangedetc.Althoughthebenefitofanefficientdatamanagementplatformcanbe
enormousnotonlyintermsofadirectcontrollingofthebusinessoracompetitive
advantage but also in terms of strategic advantages, the technical challenges are
enormoustokeepadatamanagementmachineryrunning.
In the age of “Everything-as-a-Service” using cloud-based solutions, it is
fair enough to question on-premise data management installations and consider
“Software-as-a-Service”an alternativeor complementaryconceptforsystem plat-
forms to run (parts of) a larger data management solution. Within this book, we
tackle this question to give answers to some of the huge number of different
facets, which have to be considered in this context. We will therefore review the
general conceptof the “Everything-as-a-Service”paradigm and discuss situations
where this concept may fit perfectly or where the concept is only well-suited for
specificscenarios.Thebookwillalsoreviewthegeneralrelationshipbetweencloud
computingonthe onehandanddata managementonthe otherhand.We willalso
outlinetheimpactofbeingabletoperformdatamanagementonaweb-scaleasone
ofthekeychallengesandalsorequirementsforfuturecommercialdatamanagement
platforms.
Inordertoprovideacomprehensiveunderstandingofdatamanagementservices
in the large, we will introduce the notion of virtualization as one of the key
techniquestoestablishthe“Everything-as-a-Service”-paradigm.Virtualizationcan
be exploited on a physical and logical layer to abstract from specific hardware
software environments providing an additional indirection layer which can be
vii
viii Preface
used to differentiate between services or components running remotely in cloud
environments or locally within a totally controlled system and administration
setup.Chapter2willthereforediscussdifferenttechniquesrangingfromlow-level
machinevirtualizationtoapplication-awarevirtualizationtechniquesonschemaand
applicationlevel.
In the following two chapters, we dive into detail while discussing different
methodsandtechnologiestorunweb-scaledatamanagementserviceswithspecial
emphasis on operational or analytical workloads. Operational workload on the
one hand usually consists of highly concurrent accesses to the same database
with conflict potential resulting in a requirement of transactional behavior of the
data management service. Analytical workloads on the other hand usually touch
hundreds of million of rows within a single query feeding complex statistical
models or produce the raw material for sophisticated cockpit applications within
larger Business Intelligence environments.Due to the completely differentnature
of using a data management system, we take a detailed look with respect to the
individualscenarios.Thefirstpart(Chap.3)addressingchallengesandsolutionsfor
transactionalworkloadpatternsfocusesonconsistencymodels,datafragmentation,
anddata replicationschemesfor largeweb-scale data managementscenarios.The
second part (Chap.4) outlines the major techniques and challenges to efficiently
handleanalyticsinbigdatascenariosbydiscussingprosandconsoftheMapReduce
data processing paradigm and presenting different query languages to directly
interactwiththedatamanagementplatform.
Cloud-basedservicesdonotonlyofferanefficientdataprocessingframeworkby
providingaspecificdegreeofconsistencybutalsocomprisenon-functionalservices
like considering quality of service agreements or adhere to certain security and
privacyconstraints. In Chap.5, we will outline the core requirementsand provide
insights into some already existing approaches.In a final step, the book gives the
readera state-of-the-artoverviewof currentlyavailable“everything-as-a-service”-
implementations or commercial services with respect to infrastructural services,
platformservicesorwithinthecontextofsoftwareasaservice.
Preface ix
The book closes with an outlook on challenges and open research questions
in the context of providing a data management solution following the “as-a-
service”-paradigm.Withinthisdiscussion,wereflectthedifferentdiscussionpoints
and outline future directions for a wide and open field of research potential and
commercialopportunities.
Theoverallstructureofthebookisreflectedintheaccompanyingfigure.Since
the individual sections are – with the exception of the general introduction –
independentof each other, readers are encouragedto jump into the middle of the
bookoruseadifferentpathtocapturethepresentedmaterial.Afterhavingreadthe
book,thereadersshouldhaveadeepunderstandingoftherequirementsandexisting
solutionstoestablishandoperateanefficientdatamanagementplatformfollowing
the“as-a-service”-paradigm.
Dresden,Germany WolfgangLehner
Ilmenau,Germany Kai-UweSattler