clouds data lakes, data warehouses, and the new data
TRANSCRIPT
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
Page 1 of 5 next gtgt
What types of platforms are most viable for modern data analyticsrequirements These days there are a wide variety of choices avail able toenterprises including data lakes ware houses lakehouses and other optionsmdashresident within an on-site data center or accessed via the cloud The options areboundless Itrsquos a matter of nding the best t for the business task at hand
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Choices will depend on the nature and purpose of the data ldquoTherersquos no one-size-ts-all solutionrdquo said Teresa Wingeld director of product marketing at Actian Aexi ble multi-platform approach is needed with a wide range of data capabilitiesmdashbe it a data warehouse data lake trans actional database IoT database orthird-party data service she noted
The data platform best for analytics ldquodepends on the type of information youwant to analyzerdquo said Matthew Adams senior cloud architect at ZL TechnologiesldquoIf you hold large amounts of easily categorizable structured datamdashsalesmetrics warehouse items or customer informationmdashdata warehouses are idealStructured reservoirs and warehouses cater to analytics by design as they areorganically seg mented and neatly packagedrdquo In contrast ldquoif you want to analyzeinformation that is less structuredmdashemails les or messagesmdashthen a data lakeis a better approachrdquo
MORE CHOICES
There is a third way observed Herb VanHook vice pres ident of enterprise CTOServices of BMC Software Emerg ing data lakehouses address requirements foranalyzing both structured and unstructured data he said ldquoLakehouses are a newconstruct that applies querying capabilities associ ated with structured data anddata warehouses alongside the diverse workloads that data lakes use to processmany formats Many cloud providers oer a set of dierent plat form capabilitiesincluding a data warehouse with que rying a data lake with analytics processingand managed infrastructure to run these platformsrdquo
The data lakehouse architecture oers a compelling option ldquosince it combinesthe best qualities of data warehouses and lakes to provide a single solution forall signicant data workloadsrdquo including SQL analytics business intelligence datascience and AI agreed Joel Minnick vice president of marketing at Databricks
Data warehouses ldquohave been used in the past for lagging indicators such assales of the last quarter per product linerdquo said Prashant Kelker partner for digitalstrategy and solutions at ISG ldquoThe focus is now shifting to leading indicators forexample what could the forecasted sales be per product line Leading indicatorsare making way for either judgment or prediction algorithms Data lakes and dataware houses are merging into concepts like data lakehousesrdquo
Both the data lake and data ware house are not perfect solutions Minn ick pointedout ldquoThey have contrasting benets that often require organizations to deployboth architectures which is costly to maintain complicated and causesinformation silos that slow down decision making Data lakehouses provide thelow-cost open standards support of a data lake with data ware house-likeperformance reliability quality and scale Lakehouses can sup port bothstructured and unstructured data including video audio and textrdquo
In addition the range of choices extend well beyond the bounds of enterprisesoften easily accessible via APIs which support ldquoa number of easily integrateddata orchestration platformsrdquo said Pragyansmita Nayak chief data scientist atHitachi Vantara Federal ldquoOpen and easily extensible architectures enable thisdata exchange without signicant development and learningrdquo
Page 1 of 5 next gtgt
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More White Papers
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SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 2 of 5 next gtgt
In all scenarios business require ments should be the ultimate deter minant andthis is very much the case with data analytics platforms ldquoUlti mately it is alwaysabout how well business challenges such as customer churn fraud detection orsecurity threat perception are addressedrdquo said Sri Raghavan director of datascience and advanced analytics product mar keting for Teradata ldquoData platformsthat are also accompanied by a palette of analytics capabilitiesmdashalgorithmsvisualizations workowsmdashthat can be used by a wide range of personas willalways be dominantrdquo
There are considerable choices above and beyond the data envi ronment itselfldquoTodayrsquos analytics requirements range from real-time and time-series-basedanalysis right down to standard BIrdquo said Kelker Some analytics processes are tooexpensive to be done in the cloud due to ingestion costs and therefore theyrequire edge AI solutions with local algorithms and tinyML (tiny machinelearning)
ldquoThe days of master data man agement are overrdquo Kelker stated The focus hasmoved away from data models toward algorithms he noted ldquoThe explosion of
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external data elds intermeshed with internal data makes this increasinglydicult to design upfront Modern concepts such as streaming architectures anddata meshes are blending the worlds of data storage and analytics togetherrdquo
The main issue is that ldquoorgani zations need real-time actionable insights toinform critical decision makingrdquo said Scott Gnau VP of data platforms atInterSystems ldquoSeamless cross-silo access to the right data at the right time isdicult due to increas ing complexity and latency challenges Scalable high-performance data plat forms that connect distributed data to the composablestack need to serve as the foundation of modern analytics strategiesrdquo
DATA WAREHOUSES LAKES AND EVERYTHINGIN BETWEEN
What kind of role is emerging for todayrsquos data warehouses and how have datalakes shaped this role Itrsquos important that ldquodata warehouses and data lakesoperate in unison if businesses want to stay ahead of the gamerdquo said AdamsldquoData ware houses typically ingested informa tion from relational databases thatwas then extracted by business intel ligence tools for further analysisrdquo saidAdams Data lakes have created ldquoan undercurrent for warehousesrdquo whereby theyare now able to store all business datamdashfrom contacts user informationdocuments pictures logsmdashor any data the business and its users generate henoted ldquoWhile hav ing a breadth of diverse information in data lakes makestransforming data a more dicult task it gives organiza tions a wealth ofinformation that was previously inaccessiblerdquo
A converged data warehouse-lake architecture is the best path forward forsupporting increasingly complex analytic data environments Ragha van saidldquoData lakes or data swamps require robust solutions to under stand search andanalyze the data in a context-sensitive manner while not losing the associatedlineage and provenance informationrdquo he pointed out ldquoData warehouses havebeen rearchitected to meet the emerging need of analytics that is near real-timeand can handle large volumes of datardquo
Raghavan also pointed out that ldquotodayrsquos data warehouses have become high-eciency super-compute clus ters where not only are ETL processes used todeliver clean data but also combined with state-of-the-art fea ture engineeringand modeling capa bilities to deliver high performance models andoperationalizations at scalerdquo Data lakes have contributed to these super datawarehouses ldquoby simply increasing the volume and the breadth of data that couldbe ingested into a data warehouse The presence of a loosely coupled compute-storage architecture ensures that subsets of the data can be selected for ETL[pro cesses] and more production-ready work within the warehouserdquo
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 3 of 5 next gtgt
Structured data repositories such as data warehouses may be essential withinbusinesses turning to data-in tensive approaches such as AI and machinelearning ldquoMany analyt ics jobs running in an environment like Spark createstructured features from unstructured datardquo said Van Hook ldquoAs features like thisfeed more machine learning models as well as traditional reporting the need fora structured data store to hold the composite features emerges You can imaginetraditional data warehouse capabilities evolving to hold feature tables andbecoming a high-perfor mance feature store that can drive both the training andinference activ ities of machine learning models At the bottom line warehousesbecome the querying point while lakes are the analytics pointrdquo
The data within data warehouses is generally trusted as the central version oftruth because itrsquos highly curated and processed said Anjan Kundavaram chiefproduct ocer at Precisely ldquoFor analytics the structured format of datawarehouses makes it easier for standardized access queries and reporting Thepredetermined struc ture also oers ready-to-use clean data that is ideal fororganizations that need to conduct operational analysis or reportingrdquo
The platform ldquoshould have a data fabric to drive data ow orchestra tion andautomation to deliver infor mation and intelligence to usersrdquo Wineld said ldquoTheplatform will also need shared management and secu rity services and supportfor a range of clients to meet the application development requirements fordier ent usersmdashincluding data engineers data scientists business analysts andbusiness usersrdquo
Data fabrics also oer a way to bring these environments closer together todeliver analytics as needed ldquoTodayrsquos data warehouses are collecting immenseamounts of datamdashmore than may have been anticipated when thesetechnologies were originally implementedrdquo Gnau said ldquoWhile data lakes havehelped organize this raw data into central repositories they still are not typically
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involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
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Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
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SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
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Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SIGN UP
[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
Choices will depend on the nature and purpose of the data ldquoTherersquos no one-size-ts-all solutionrdquo said Teresa Wingeld director of product marketing at Actian Aexi ble multi-platform approach is needed with a wide range of data capabilitiesmdashbe it a data warehouse data lake trans actional database IoT database orthird-party data service she noted
The data platform best for analytics ldquodepends on the type of information youwant to analyzerdquo said Matthew Adams senior cloud architect at ZL TechnologiesldquoIf you hold large amounts of easily categorizable structured datamdashsalesmetrics warehouse items or customer informationmdashdata warehouses are idealStructured reservoirs and warehouses cater to analytics by design as they areorganically seg mented and neatly packagedrdquo In contrast ldquoif you want to analyzeinformation that is less structuredmdashemails les or messagesmdashthen a data lakeis a better approachrdquo
MORE CHOICES
There is a third way observed Herb VanHook vice pres ident of enterprise CTOServices of BMC Software Emerg ing data lakehouses address requirements foranalyzing both structured and unstructured data he said ldquoLakehouses are a newconstruct that applies querying capabilities associ ated with structured data anddata warehouses alongside the diverse workloads that data lakes use to processmany formats Many cloud providers oer a set of dierent plat form capabilitiesincluding a data warehouse with que rying a data lake with analytics processingand managed infrastructure to run these platformsrdquo
The data lakehouse architecture oers a compelling option ldquosince it combinesthe best qualities of data warehouses and lakes to provide a single solution forall signicant data workloadsrdquo including SQL analytics business intelligence datascience and AI agreed Joel Minnick vice president of marketing at Databricks
Data warehouses ldquohave been used in the past for lagging indicators such assales of the last quarter per product linerdquo said Prashant Kelker partner for digitalstrategy and solutions at ISG ldquoThe focus is now shifting to leading indicators forexample what could the forecasted sales be per product line Leading indicatorsare making way for either judgment or prediction algorithms Data lakes and dataware houses are merging into concepts like data lakehousesrdquo
Both the data lake and data ware house are not perfect solutions Minn ick pointedout ldquoThey have contrasting benets that often require organizations to deployboth architectures which is costly to maintain complicated and causesinformation silos that slow down decision making Data lakehouses provide thelow-cost open standards support of a data lake with data ware house-likeperformance reliability quality and scale Lakehouses can sup port bothstructured and unstructured data including video audio and textrdquo
In addition the range of choices extend well beyond the bounds of enterprisesoften easily accessible via APIs which support ldquoa number of easily integrateddata orchestration platformsrdquo said Pragyansmita Nayak chief data scientist atHitachi Vantara Federal ldquoOpen and easily extensible architectures enable thisdata exchange without signicant development and learningrdquo
Page 1 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
More White Papers
SPONSORS
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
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IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 2 of 5 next gtgt
In all scenarios business require ments should be the ultimate deter minant andthis is very much the case with data analytics platforms ldquoUlti mately it is alwaysabout how well business challenges such as customer churn fraud detection orsecurity threat perception are addressedrdquo said Sri Raghavan director of datascience and advanced analytics product mar keting for Teradata ldquoData platformsthat are also accompanied by a palette of analytics capabilitiesmdashalgorithmsvisualizations workowsmdashthat can be used by a wide range of personas willalways be dominantrdquo
There are considerable choices above and beyond the data envi ronment itselfldquoTodayrsquos analytics requirements range from real-time and time-series-basedanalysis right down to standard BIrdquo said Kelker Some analytics processes are tooexpensive to be done in the cloud due to ingestion costs and therefore theyrequire edge AI solutions with local algorithms and tinyML (tiny machinelearning)
ldquoThe days of master data man agement are overrdquo Kelker stated The focus hasmoved away from data models toward algorithms he noted ldquoThe explosion of
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
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[ LATEST ISSUE ]
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external data elds intermeshed with internal data makes this increasinglydicult to design upfront Modern concepts such as streaming architectures anddata meshes are blending the worlds of data storage and analytics togetherrdquo
The main issue is that ldquoorgani zations need real-time actionable insights toinform critical decision makingrdquo said Scott Gnau VP of data platforms atInterSystems ldquoSeamless cross-silo access to the right data at the right time isdicult due to increas ing complexity and latency challenges Scalable high-performance data plat forms that connect distributed data to the composablestack need to serve as the foundation of modern analytics strategiesrdquo
DATA WAREHOUSES LAKES AND EVERYTHINGIN BETWEEN
What kind of role is emerging for todayrsquos data warehouses and how have datalakes shaped this role Itrsquos important that ldquodata warehouses and data lakesoperate in unison if businesses want to stay ahead of the gamerdquo said AdamsldquoData ware houses typically ingested informa tion from relational databases thatwas then extracted by business intel ligence tools for further analysisrdquo saidAdams Data lakes have created ldquoan undercurrent for warehousesrdquo whereby theyare now able to store all business datamdashfrom contacts user informationdocuments pictures logsmdashor any data the business and its users generate henoted ldquoWhile hav ing a breadth of diverse information in data lakes makestransforming data a more dicult task it gives organiza tions a wealth ofinformation that was previously inaccessiblerdquo
A converged data warehouse-lake architecture is the best path forward forsupporting increasingly complex analytic data environments Ragha van saidldquoData lakes or data swamps require robust solutions to under stand search andanalyze the data in a context-sensitive manner while not losing the associatedlineage and provenance informationrdquo he pointed out ldquoData warehouses havebeen rearchitected to meet the emerging need of analytics that is near real-timeand can handle large volumes of datardquo
Raghavan also pointed out that ldquotodayrsquos data warehouses have become high-eciency super-compute clus ters where not only are ETL processes used todeliver clean data but also combined with state-of-the-art fea ture engineeringand modeling capa bilities to deliver high performance models andoperationalizations at scalerdquo Data lakes have contributed to these super datawarehouses ldquoby simply increasing the volume and the breadth of data that couldbe ingested into a data warehouse The presence of a loosely coupled compute-storage architecture ensures that subsets of the data can be selected for ETL[pro cesses] and more production-ready work within the warehouserdquo
ltlt back Page 2 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
More White Papers
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SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
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5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 3 of 5 next gtgt
Structured data repositories such as data warehouses may be essential withinbusinesses turning to data-in tensive approaches such as AI and machinelearning ldquoMany analyt ics jobs running in an environment like Spark createstructured features from unstructured datardquo said Van Hook ldquoAs features like thisfeed more machine learning models as well as traditional reporting the need fora structured data store to hold the composite features emerges You can imaginetraditional data warehouse capabilities evolving to hold feature tables andbecoming a high-perfor mance feature store that can drive both the training andinference activ ities of machine learning models At the bottom line warehousesbecome the querying point while lakes are the analytics pointrdquo
The data within data warehouses is generally trusted as the central version oftruth because itrsquos highly curated and processed said Anjan Kundavaram chiefproduct ocer at Precisely ldquoFor analytics the structured format of datawarehouses makes it easier for standardized access queries and reporting Thepredetermined struc ture also oers ready-to-use clean data that is ideal fororganizations that need to conduct operational analysis or reportingrdquo
The platform ldquoshould have a data fabric to drive data ow orchestra tion andautomation to deliver infor mation and intelligence to usersrdquo Wineld said ldquoTheplatform will also need shared management and secu rity services and supportfor a range of clients to meet the application development requirements fordier ent usersmdashincluding data engineers data scientists business analysts andbusiness usersrdquo
Data fabrics also oer a way to bring these environments closer together todeliver analytics as needed ldquoTodayrsquos data warehouses are collecting immenseamounts of datamdashmore than may have been anticipated when thesetechnologies were originally implementedrdquo Gnau said ldquoWhile data lakes havehelped organize this raw data into central repositories they still are not typically
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
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TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
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5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
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DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
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NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
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Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
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Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 2 of 5 next gtgt
In all scenarios business require ments should be the ultimate deter minant andthis is very much the case with data analytics platforms ldquoUlti mately it is alwaysabout how well business challenges such as customer churn fraud detection orsecurity threat perception are addressedrdquo said Sri Raghavan director of datascience and advanced analytics product mar keting for Teradata ldquoData platformsthat are also accompanied by a palette of analytics capabilitiesmdashalgorithmsvisualizations workowsmdashthat can be used by a wide range of personas willalways be dominantrdquo
There are considerable choices above and beyond the data envi ronment itselfldquoTodayrsquos analytics requirements range from real-time and time-series-basedanalysis right down to standard BIrdquo said Kelker Some analytics processes are tooexpensive to be done in the cloud due to ingestion costs and therefore theyrequire edge AI solutions with local algorithms and tinyML (tiny machinelearning)
ldquoThe days of master data man agement are overrdquo Kelker stated The focus hasmoved away from data models toward algorithms he noted ldquoThe explosion of
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
SIGN UP
[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
external data elds intermeshed with internal data makes this increasinglydicult to design upfront Modern concepts such as streaming architectures anddata meshes are blending the worlds of data storage and analytics togetherrdquo
The main issue is that ldquoorgani zations need real-time actionable insights toinform critical decision makingrdquo said Scott Gnau VP of data platforms atInterSystems ldquoSeamless cross-silo access to the right data at the right time isdicult due to increas ing complexity and latency challenges Scalable high-performance data plat forms that connect distributed data to the composablestack need to serve as the foundation of modern analytics strategiesrdquo
DATA WAREHOUSES LAKES AND EVERYTHINGIN BETWEEN
What kind of role is emerging for todayrsquos data warehouses and how have datalakes shaped this role Itrsquos important that ldquodata warehouses and data lakesoperate in unison if businesses want to stay ahead of the gamerdquo said AdamsldquoData ware houses typically ingested informa tion from relational databases thatwas then extracted by business intel ligence tools for further analysisrdquo saidAdams Data lakes have created ldquoan undercurrent for warehousesrdquo whereby theyare now able to store all business datamdashfrom contacts user informationdocuments pictures logsmdashor any data the business and its users generate henoted ldquoWhile hav ing a breadth of diverse information in data lakes makestransforming data a more dicult task it gives organiza tions a wealth ofinformation that was previously inaccessiblerdquo
A converged data warehouse-lake architecture is the best path forward forsupporting increasingly complex analytic data environments Ragha van saidldquoData lakes or data swamps require robust solutions to under stand search andanalyze the data in a context-sensitive manner while not losing the associatedlineage and provenance informationrdquo he pointed out ldquoData warehouses havebeen rearchitected to meet the emerging need of analytics that is near real-timeand can handle large volumes of datardquo
Raghavan also pointed out that ldquotodayrsquos data warehouses have become high-eciency super-compute clus ters where not only are ETL processes used todeliver clean data but also combined with state-of-the-art fea ture engineeringand modeling capa bilities to deliver high performance models andoperationalizations at scalerdquo Data lakes have contributed to these super datawarehouses ldquoby simply increasing the volume and the breadth of data that couldbe ingested into a data warehouse The presence of a loosely coupled compute-storage architecture ensures that subsets of the data can be selected for ETL[pro cesses] and more production-ready work within the warehouserdquo
ltlt back Page 2 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
More White Papers
SPONSORS
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
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DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 3 of 5 next gtgt
Structured data repositories such as data warehouses may be essential withinbusinesses turning to data-in tensive approaches such as AI and machinelearning ldquoMany analyt ics jobs running in an environment like Spark createstructured features from unstructured datardquo said Van Hook ldquoAs features like thisfeed more machine learning models as well as traditional reporting the need fora structured data store to hold the composite features emerges You can imaginetraditional data warehouse capabilities evolving to hold feature tables andbecoming a high-perfor mance feature store that can drive both the training andinference activ ities of machine learning models At the bottom line warehousesbecome the querying point while lakes are the analytics pointrdquo
The data within data warehouses is generally trusted as the central version oftruth because itrsquos highly curated and processed said Anjan Kundavaram chiefproduct ocer at Precisely ldquoFor analytics the structured format of datawarehouses makes it easier for standardized access queries and reporting Thepredetermined struc ture also oers ready-to-use clean data that is ideal fororganizations that need to conduct operational analysis or reportingrdquo
The platform ldquoshould have a data fabric to drive data ow orchestra tion andautomation to deliver infor mation and intelligence to usersrdquo Wineld said ldquoTheplatform will also need shared management and secu rity services and supportfor a range of clients to meet the application development requirements fordier ent usersmdashincluding data engineers data scientists business analysts andbusiness usersrdquo
Data fabrics also oer a way to bring these environments closer together todeliver analytics as needed ldquoTodayrsquos data warehouses are collecting immenseamounts of datamdashmore than may have been anticipated when thesetechnologies were originally implementedrdquo Gnau said ldquoWhile data lakes havehelped organize this raw data into central repositories they still are not typically
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
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WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
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TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
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Facebook Comments Plugin
Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
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5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
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IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
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DATA SUMMITCONFERENCE NEWS
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The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
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Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SIGN UP
[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
external data elds intermeshed with internal data makes this increasinglydicult to design upfront Modern concepts such as streaming architectures anddata meshes are blending the worlds of data storage and analytics togetherrdquo
The main issue is that ldquoorgani zations need real-time actionable insights toinform critical decision makingrdquo said Scott Gnau VP of data platforms atInterSystems ldquoSeamless cross-silo access to the right data at the right time isdicult due to increas ing complexity and latency challenges Scalable high-performance data plat forms that connect distributed data to the composablestack need to serve as the foundation of modern analytics strategiesrdquo
DATA WAREHOUSES LAKES AND EVERYTHINGIN BETWEEN
What kind of role is emerging for todayrsquos data warehouses and how have datalakes shaped this role Itrsquos important that ldquodata warehouses and data lakesoperate in unison if businesses want to stay ahead of the gamerdquo said AdamsldquoData ware houses typically ingested informa tion from relational databases thatwas then extracted by business intel ligence tools for further analysisrdquo saidAdams Data lakes have created ldquoan undercurrent for warehousesrdquo whereby theyare now able to store all business datamdashfrom contacts user informationdocuments pictures logsmdashor any data the business and its users generate henoted ldquoWhile hav ing a breadth of diverse information in data lakes makestransforming data a more dicult task it gives organiza tions a wealth ofinformation that was previously inaccessiblerdquo
A converged data warehouse-lake architecture is the best path forward forsupporting increasingly complex analytic data environments Ragha van saidldquoData lakes or data swamps require robust solutions to under stand search andanalyze the data in a context-sensitive manner while not losing the associatedlineage and provenance informationrdquo he pointed out ldquoData warehouses havebeen rearchitected to meet the emerging need of analytics that is near real-timeand can handle large volumes of datardquo
Raghavan also pointed out that ldquotodayrsquos data warehouses have become high-eciency super-compute clus ters where not only are ETL processes used todeliver clean data but also combined with state-of-the-art fea ture engineeringand modeling capa bilities to deliver high performance models andoperationalizations at scalerdquo Data lakes have contributed to these super datawarehouses ldquoby simply increasing the volume and the breadth of data that couldbe ingested into a data warehouse The presence of a loosely coupled compute-storage architecture ensures that subsets of the data can be selected for ETL[pro cesses] and more production-ready work within the warehouserdquo
ltlt back Page 2 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
More White Papers
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SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
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DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 3 of 5 next gtgt
Structured data repositories such as data warehouses may be essential withinbusinesses turning to data-in tensive approaches such as AI and machinelearning ldquoMany analyt ics jobs running in an environment like Spark createstructured features from unstructured datardquo said Van Hook ldquoAs features like thisfeed more machine learning models as well as traditional reporting the need fora structured data store to hold the composite features emerges You can imaginetraditional data warehouse capabilities evolving to hold feature tables andbecoming a high-perfor mance feature store that can drive both the training andinference activ ities of machine learning models At the bottom line warehousesbecome the querying point while lakes are the analytics pointrdquo
The data within data warehouses is generally trusted as the central version oftruth because itrsquos highly curated and processed said Anjan Kundavaram chiefproduct ocer at Precisely ldquoFor analytics the structured format of datawarehouses makes it easier for standardized access queries and reporting Thepredetermined struc ture also oers ready-to-use clean data that is ideal fororganizations that need to conduct operational analysis or reportingrdquo
The platform ldquoshould have a data fabric to drive data ow orchestra tion andautomation to deliver infor mation and intelligence to usersrdquo Wineld said ldquoTheplatform will also need shared management and secu rity services and supportfor a range of clients to meet the application development requirements fordier ent usersmdashincluding data engineers data scientists business analysts andbusiness usersrdquo
Data fabrics also oer a way to bring these environments closer together todeliver analytics as needed ldquoTodayrsquos data warehouses are collecting immenseamounts of datamdashmore than may have been anticipated when thesetechnologies were originally implementedrdquo Gnau said ldquoWhile data lakes havehelped organize this raw data into central repositories they still are not typically
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
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TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
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Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
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Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 3 of 5 next gtgt
Structured data repositories such as data warehouses may be essential withinbusinesses turning to data-in tensive approaches such as AI and machinelearning ldquoMany analyt ics jobs running in an environment like Spark createstructured features from unstructured datardquo said Van Hook ldquoAs features like thisfeed more machine learning models as well as traditional reporting the need fora structured data store to hold the composite features emerges You can imaginetraditional data warehouse capabilities evolving to hold feature tables andbecoming a high-perfor mance feature store that can drive both the training andinference activ ities of machine learning models At the bottom line warehousesbecome the querying point while lakes are the analytics pointrdquo
The data within data warehouses is generally trusted as the central version oftruth because itrsquos highly curated and processed said Anjan Kundavaram chiefproduct ocer at Precisely ldquoFor analytics the structured format of datawarehouses makes it easier for standardized access queries and reporting Thepredetermined struc ture also oers ready-to-use clean data that is ideal fororganizations that need to conduct operational analysis or reportingrdquo
The platform ldquoshould have a data fabric to drive data ow orchestra tion andautomation to deliver infor mation and intelligence to usersrdquo Wineld said ldquoTheplatform will also need shared management and secu rity services and supportfor a range of clients to meet the application development requirements fordier ent usersmdashincluding data engineers data scientists business analysts andbusiness usersrdquo
Data fabrics also oer a way to bring these environments closer together todeliver analytics as needed ldquoTodayrsquos data warehouses are collecting immenseamounts of datamdashmore than may have been anticipated when thesetechnologies were originally implementedrdquo Gnau said ldquoWhile data lakes havehelped organize this raw data into central repositories they still are not typically
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
involved in opera tional and transactional data ows This is where modern dataarchitec tures such as data fabrics come into play Not only do data fabrics eec -tively organize the datasets into elds that help identify the most actionable andhigh-quality resources but each one [also] tends to meet a unique IT-drivenpurpose Without a well-or chestrated architecture the data remains eitherinaccessible and wasted or not eciently addressable regard less of where it sitswithin the data lake or warehouserdquo
TO THE CLOUDmdashIN MOST CASES
Are analytical platforms such as data warehouses lakes or lakehouses going tothe cloud Are there scenar ios where on-premise approaches are still preferableA recent survey of IT leaders found that the major ity 53 see hybrid or multi-cloud data warehousing as one of the most important data warehousing-re latedtrends of this yearmdashmore than any other trend The question isnrsquot really aboutldquowhyrdquo to use cloud any more said Minnick ldquoIncreasingly wersquore seeing customersnow ponder lsquowhichrsquo cloudsrdquo Minnick noted that the majority of Databricksrsquoenterprise customers work with at least two cloud providers today ldquoAs a resultitrsquos become much more important that organizations adopt solutions that oer aconsistent experience for their employees regardless of where the data residesrdquo
ltlt back Page 3 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 4 of 5 next gtgt
Moving to the cloud may only be a matter of keeping up with the rest of theenvironment ldquoData regardless of the type is already on the cloudrdquo saidRaghavan ldquoEvery enterprise cus tomer that I have worked with has petabytes ofinformation stored in the cloud and has multiple clouds for disaster recoveryfault resistance geographical duplication as well as workload segmentationamong other thingsrdquo However there are still reasons why a part of an enter -prisersquos workload will continue to stay on-premise he continued Security for onemay require keeping data within the enterprise walls This does not assume thatthere are no risks of security breaches in the on-premise world Raghavanstressed It is just that companies feel a bit more at ease with the perception ofcomplete con trol of their on-premise installations
There are other reasons to at least keep analytics data on-prem ise as wellmdashsuchas ldquodata transit costs latency for real-time apps and cost at scalerdquo saidVanHook ldquoThis is true in cases where compliance requires complete control ofthe data infrastructure as well as micro data centers on the edge thatpreprocess substantial amounts of data from remote locationsrdquo On-premiseware houses are also still desirable for large-scale purpose-built high-per -formance infrastructures for spe cialized applications VanHook said ldquoExamplesof this include a real-time data warehouse for a customer-fac ing service and thereal-time routing app for a delivery servicerdquo
Still the mass migration of data environments continues toward the cloud Whileon-premise data man agement ldquooers the ultimate control over the location ofdata hardware software and who has access it can be expensive and dicult toscalerdquo said Kundavaram On-premise data management requires constant main -tenance time-consuming upgrades and potentially outage managementKundavaram continued ldquoOn the other hand companies that operate their datalakes and data warehouses in the cloud see a nimbler experience Oper ating inthe cloud allows businesses to accumulate massive amounts of data and scale
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
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5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud ComputingData Center Management
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in DataTrend Setting Products in Data andInformation Management
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorldOnline Searcher
their operations accordingly Data lakes and warehouses that oper ate in thecloud can manage multiple data streams simultaneously while backing up thisdata automaticallyrdquo
WHATrsquoS AHEAD
Automation is making data analyt ics better Looking into the immediate andlonger-term future convergence between analytical data platforms will continueenhanced by automation capabilities industry observers state ldquoAutomation isdrastically changing the ways that we consume and process datardquo said AdamsldquoMachine learn ing mitigates many of the diculties associated with applyingdata science and review principles when processing and storing data If machinelearning gets to the point where information is streamed while processed datalakes will transition into data warehouses where analytics can be conducted inreal time Eectively machine learn ing holds the potential to structure theunstructured and open up a world of new analytic potentialrdquo
ltlt back Page 4 of 5 next gtgt
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
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Oldest
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BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
SUBSCRIBE ABOUT DATA SUMMIT BIG DATA QUARTERLY EVENTS ADVERTISE SPONSORS
Join us October 14th for a Special Live Webinar Importance of Unifying Backup amp Recovery with SaaS
SIGN UP
NEWSLETTERS
5 MINUTE BRIEFINGINFORMATIONMANAGEMENT[ LATEST ISSUE ]
5 MINUTE BRIEFINGDATA CENTER[ LATEST ISSUE ]
5 MINUTE BRIEFINGMULTIVALUE[ LATEST ISSUE ]
5 MINUTE BRIEFINGORACLE[ LATEST ISSUE ]
5 MINUTE BRIEFINGSAP[ LATEST ISSUE ]
5 MINUTE BRIEFINGBLOCKCHAIN[ LATEST ISSUE ]
5 MINUTE BRIEFINGCLOUD[ LATEST ISSUE ]
IOUG E-BRIEF ORACLEENTERPRISE MANAGER[ LATEST ISSUE ]
IOUG E-BRIEF CLOUDSTRATEGIES[ LATEST ISSUE ]
DBTA E-EDITION[ LATEST ISSUE ]
DATA SUMMITCONFERENCE NEWS
COGNITIVECOMPUTING SUMMITNEWS
The New Data Analytics Riding onData Lakes Data Warehouses andCloudsOct 5 2021By Joe McKendrick
ltlt back Page 5 of 5
While these forces converge ldquowe believe that the days of maintaining both datawarehouses and data lakes are limitedrdquo said Minnick ldquoThe lines between dataengineering data ana lytics and data science are becoming increasingly blurryThe people in these elds are working more closely together than ever drivingthe need to have everyone collaborating on the same datardquo As a result hepredicted ldquoa data lakehouse will be the norm since it builds on the open datalake where most businesses already store the majority of their data The datalakehouse also adds the transactional support and performance of a datawarehouse needed for analytics that data lakes never delivered The uni edapproach of the lakehouse will enable better collaboration with all data teammembers to work from the same data instead of their own siloed versionsrdquo
Kelker also sees movement toward data mesh and streaming architecturesplaying an increasing role in analyt ics environments ldquoA lot of this design will beimpacted by forces that pull in both directionsmdash5G and multi-access edgecomputing concepts can suddenly make connected use cases with large datapossible At the same time the advancement of edge AI tinyML and smarterprocessing chips with low power will remove the need for all data to betransmitted to the cloud Both forces are competing with how much data needsto be transferred to and from the cloudrdquo
Overall there will be capabilities around ldquodata and analytics commod itizing in thecloudrdquo said Stijn ldquoStanrdquo Christiaens co-founder and chief data citizen at CollibraldquoWhile the drivers for that are strong and growth is fast it will still take time fororganizations to fully adopt and migrate to a modern infrastructure while at thesame time lsquokeeping the lights onrsquo [in] the oldrdquo He also foresees more focus onprivacy-en hancing technologies such as encryp tion that keep patterns in dataalive for analytics or synthetic data as well as greater focus on real-time usecases for example with time-series approaches
WHITE PAPERS
EBOOK TOP 10PRINCIPLES OF A CLOUDBACKUP SERVICE
WHY YOUR POSTGRESQLDATABASES SHOULD LIVEON AMAZON AURORA
MODERNIZING DATAMANAGEMENT FOR THEHYBRID MULTI-CLOUDWORLD
SLASH CLOUD BACKUP COSTS WITH QUESTregQORESTORreg
OVERCOMING GAPS IN OFFICE 365 DATAPROTECTION PAPER
More White Papers
SPONSORS
NEWS ANALYSIS WHITE PAPERS WEBINARS RESOURCES RESEARCH VIDEOS
TOPICS BIG DATA BI amp ANALYTICS DATA INTEGRATION DATABASE MANAGEMENT VIRTUALIZATION CLOUD MORE TOPICS
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg
Database Trends and Applicationsdelivers news and analysis on bigdata data science analytics and theworld of information management
Topics Resources Other ITI SitesBig DataBusiness Intelligence and AnalyticsData IntegrationDatabase ManagementVirtualizationCloud Computing
DBTA E-EditionData and Information ManagementNewslettersDBTA 100 The 100 Companies thatMatter in Data
Destination CRMFaulkner Information ServicesInfoTodaycomInfoToday EuropeITIResearchcomKMWorld
Wingeld sees a rise in containerized analytics architectures which makesanalytics capabilities ldquomore composable so that they can be more exibly com -bined into applicationsrdquo In addition data lake and data warehouse architec turesldquowill also need to be containerized to meet the resource demands associ atedwith big data articial intelligence machine learning streaming analytics andother resource-intensive decision intelligence tools that are straining older datalake architecturesrdquo
There will also be a push to democ ratize data analytics as these platforms openup and become more ubiquitous within enterprises ldquoThe initial perso nas whowere doing a lot of work on data lakes were architects and hard core datascientists who were able to create complex analytic pipelines with multipleprogramming frameworksrdquo said Raghavan ldquoThis was a hard-to-rep licate corecompetency that resulted in a hard-to-justify mystiquerdquo This will change withmore use cases more types of users and more applications derived from datalakes said Raghavan
ltlt back Page 5 of 5
Subscribe to Database Trends and Applications Magazine
PUBLICATIONS amp REPORTS
0 Comments Sort by
Facebook Comments Plugin
Oldest
Add a comment
BUILDING A CULTUREOF TRUST IN ACOMPETITIVEECONOMY 2021SURVEY ON DATAQUALITY
EBOOK TOP 10PRINCIPLES OF ACLOUD BACKUPSERVICE
WHY YOURPOSTGRESQLDATABASES SHOULDLIVE ON AMAZONAURORA
MODERNIZING DATAMANAGEMENT FORTHE HYBRID MULTI-CLOUD WORLD
SLASH CLOUDBACKUP COSTS WITHQUESTreg QORESTORreg