Index []€¦ · Amazon Microsoft SQL Server instances, 142 Amazon Mobile Analytics, 149–151 Amazon Mobile Hub, 149 Amazon Mobile SDK, 149, 150 Amazon OpsWorks, 137, 139 Amazon

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Index

3-E test. See Effective, efficient,and economical

5Rs. See Retire Replace Retainand wrap Re-hostRe-envision

6Rs. See Retire Retain Re-hostRe-platform Re-purchaseRe-factor

AAccess control lists (ACLs), 173,

194Active Directory Lightweight

Directory Services, 141Active geo-replication

(availability feature), 184AD. See Amazon Microsoft Active

DirectoryAdaptability, 82ADF. See Azure Data FactoryADLS. See Azure Data Lake StoreAdvanced analytics, 226, 246

cloud/data, combination, 39fleverage, 251–252machine learning/artificial

intelligence, leveraging, 41usage, 248

AES 256-bit encryption, usage,171

AKS. See Azure Container ServiceAmazon API Gateway, 151–152Amazon AppStream 2.0, 154,

155Amazon Athena, 144Amazon Aurora, 128–129,

132

Amazon Certificate Manager,140, 142

Amazon Chime, 153, 154Amazon Cloud Directory, 140,

141Amazon CloudFormation, 137,

138Amazon CloudFront, 133, 134Amazon CloudHSM, 140, 142Amazon CloudSearch, 144, 145Amazon cloud service, response

values, 138Amazon CloudTrail, 137, 138,

143Amazon CloudWatch, 137Amazon CodeBuild, 136Amazon CodeCommit, 136Amazon CodeDeploy, 136Amazon CodePipeline, 136Amazon Cognito, 149–150Amazon Config, 137, 139Amazon Dash, button (usage),

158Amazon Data Pipeline, 144,

146–147Amazon Device Farm, 149, 150Amazon Direct Connect, 134,

135Amazon Directory Service, 140,

142Amazon DynamoDB, 128,

130–131, 144–145, 150, 157Amazon Elasticache, 128, 131Amazon Elastic Beanstalk, 124Amazon Elastic Block Store

(EBS), 125, 126–127

293

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HTED M

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Amazon Elastic Compute Cloud(EC2), 118, 119–122

instances, 135, 136on-demand pricing, examples,

121f, 123Systems Manager, 137,

138Amazon Elastic Compute

Container Registry (ECR),118, 122

Amazon Elastic ComputeContainer Service (ECS),118, 122

Amazon Elastic File System(EFS), 125, 127

Amazon Elastic Load Balancers,143

Amazon Elastic Load Balancing(ELB), 134, 135

Amazon Elastic Map Reduce(EMR), 144–146

Amazon Elasticsearch Service,144, 145

Amazon Elastic Transcoder, 151,152

Amazon GameLift, 158Amazon Glacier, 125, 127Amazon Glue, 144, 147Amazon Greengrass, 157Amazon Identity and Access

Management (IAM), 140,141

Amazon Inspector, 140, 142Amazon IoT Button, 157, 158Amazon IoT Platform, 157Amazon Key Management

Service (KMS), 140, 143Amazon Kinesis, 144–146, 157Amazon Lambda, 118, 124, 146,

150, 157Amazon Lex, 147, 148Amazon Lightsail, 118, 124Amazon Lumberyard, 158

Amazon Machine Learning (ML),147, 150, 157

Amazon-managed cloud service,136

Amazon Managed Services, 137Amazon Management Console,

138Amazon Microsoft Active

Directory (AD), 142Amazon Microsoft SQL Server

instances, 142Amazon Mobile Analytics,

149–151Amazon Mobile Hub, 149Amazon Mobile SDK, 149, 150Amazon OpsWorks, 137, 139Amazon Organizations, 141, 143Amazon Personal Health

Dashboard, 137Amazon Pinpoint, 149, 150Amazon Polly, 147, 148Amazon Prime Photos, 148Amazon QuickSight, 144, 146Amazon Redshift, 132, 144, 145,

151Amazon Rekognition, 147,

148Amazon Relational Database

Service (RDS), 128, 129Amazon Route 53, 134, 135Amazon S3, 144–145, 150, 151,

153, 157Amazon Service Catalogue, 137,

139Amazon Shield, 141, 143Amazon Simple Email Service

(SES), 152Amazon Simple Notification

Service (SNS), 151, 152Amazon Simple Queue Service

(SQS), 151, 152Amazon Simple Storage Service

(S3), 125, 126, 135


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Amazon Simple Workflow(SWF), 151, 152

Amazon Step Functions, 151Amazon Storage Gateway, 125,

127–128Amazon Trusted Advisor, 137,

139–140Amazon Virtual Private Cloud

(VPC), 133, 134, 142Amazon Web Application

Firewall (WAF), 141, 143Amazon Web Services (AWS),

118, 2256Rs, 100–101Application Discovery, 131,

132Cloud Adoption Framework

(CAF), 101, 103tcloud migration (6Rs), 102fcompute classification,

118–125cost calculation, example,

69tDatabase Migration Service,

131, 132EMR, 146global regions, 125fjoint partnership, 52Server Migration Service

(SMS), 131, 132shared responsibility model,

72fSLA commitment, provision,

120Snowball, 131, 133Snowball Edge, 131, 133Snowball Mobile, 131Snowmobile, 133spot instances, 68

Amazon Web Services (AWS)cloud

framework approach, 101portfolio categories, 119f

provider, 54, 61service portfolio, 159f

Amazon WorkDocs, 153–154Amazon WorkMail, 153, 154Amazon Workspaces, 154Amazon X-Ray, 136, 137AML. See Azure Machine

LearningAnalytics, 144–147, 181

benefits, 41types/maturity, 22fusage, 9, 21–29, 41

Analytics unit (AU), 194Android

applications, 213, 214mobile application usage, 150tablets, usage, 154

Apache Accumulo, 60Apache Ambari, 55Apache Apex, 58Apache Avro, 57Apache Beam, 59Apache Calcite, 59Apache Cassandra, 58Apache Falcon, 57Apache Flink, 56Apache Flume, 59Apache GraphX (graph

computation), 196Apache Hadoop, 57Apache Hama, 59Apache HBase, 58Apache Hive, 58Apache Kafka, 56Apache MapReduce, 57Apache Mesos, 56, 180–181Apache MLlib (machine

learning), 196Apache Oozie, 57Apache ORC, 60Apache Parquet, 60–61Apache Phoenix, 60Apache Pig, 58


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Apache REEF, 59Apache Samza, 56Apache Spark, 56–57Apache Spark SQL, 196Apache Squoop, 56Apache Storm, 56Apache Tajo, 59Apache Tez, 58Apache YARN, 57, 193Apache Zeppelin, 60Apache Zookeeper, 60API. See Application

programming interfaceAPN. See Apple Push NotificationAppend Blob, 171Apple macOS, usage, 154Apple Push Notification (APN),

178, 207Application

analytics, 149application-level provided

information, 135content delivery, 149services, 151–152streaming, 154–155

Application programminginterface (API)

Amazon design, 119calls, usage, 138, 152Gateway, usage, 151–152request, person/service

identification, 138support, 187usage, 103

Archiving needs, impact, 251ARIMA, usage, 217ARM. See Azure Resource

ManagerArtificial intelligence (AI),

147–151, 161, 191–200,203

cognitive services, relationship,200–203

example, 24fleveraging, 41

AS2, 207ASR. See Automating speech

recognitionAt-least-once processing, 153AU. See Analytics unitAustralian InfoSec Registered

Assessors Program, 160Authentication capabilities,

179Automatic backups (availability

feature), 184Automating speech recognition

(ASR), 148Availability Zones (AZs), 118,

126Aviva, 167Azure, 61, 159

cloud platform, 203low-priority VMs, 68portal screenshot, 73f, 74fSQL Server 2016 Tabular

Models, deployment, 198Azure Active Directory (AAD)

(AD), 179, 195, 210–212Azure Advisor, 215Azure AI and Cognitive Services,

200–201Azure Analysis Services, 192,

198–199Azure Apache Spark (for Azure

Insight), 192, 196–197Azure API Management, 175,

177–178, 213, 214Azure Application Gateway, 164,

167, 168Azure Application Insights, 213,

214, 215Azure App Service, 162,

164–165, 175–176, 178, 179Azure Archive Storage, 170Azure Automation, 215


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Azure Automation and Control,215

Azure Autoscale, 164Azure Backup, 171, 174, 215Azure Batch, 162, 166, 178, 179,

201Azure Blob Storage, 170, 171,

217Azure Bot Service, 191, 193, 200Azure Cloud Service, 162,

165–166Azure Cloud Shell, 215Azure Cognitive Services, 201Azure Container Instances, 162,

165–166, 178Azure Container Registry, 166,

178–180Azure Container Service (AKS),

162, 165, 180–181Azure Content Delivery Network

(CDN), 167, 168–169, 175,176

Azure Cosmos DB, 181, 186–190,204, 206

consistency levels, 189NoSQL database support,

187Azure Cost Management, 215Azure Custom Speech Service,

192, 199Azure Database

Migration Service, 181, 191usage, 181, 185

Azure Data Catalog, 192, 196Azure Data Factory (ADF), 186,

191, 195, 200, 217Azure Data Lake Analytics, 191,

193–194Azure Data Lake Store (ADLS),

171, 172–173, 194–195Azure DDoS Protection, 167, 170Azure DevTest Labos, 213, 214Azure Disk Storage, 170, 172

Azure Domain Name System(Azure DNS), 167, 168

Azure Event Grid, 204, 206–207Azure Event Hubs, 192, 193,

199, 204, 207–208Azure ExpressRoute, 167,

169–170Azure File Storage, 170, 172Azure Functions, 162, 165Azure Government,

ExpressRoute circuit(creation), 170

Azure HDInsight, 100, 173, 186,191, 192, 194, 196

Azure HockeyApp, 213, 214Azure Hybrid, 164Azure Insight, 192, 215Azure IoT Edge, 193, 203, 205Azure IoT Hub, 203, 204, 205Azure Key Vault, 173, 210,

211Azure Load Balancer, 164, 167,

168Azure Location-Based Services,

204, 208Azure Log Analytics, 192, 196,

215Azure Logic Apps, 204, 207Azure Machine Learning (AML)

(ML), 196, 213, 217Services, 200Studio, 200, 203, 206

Azure Managed Applications,215

Azure Marketplace, 167Azure Media Services, 175,

176–177Azure Migrate, 215Azure Mobile app, 215Azure Monitor, 215


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Azure Multi-FactorAuthentication, 210,212–213

Azure Network Watcher, 167,170, 215

Azure Notification Hubs, 175,178, 204, 207

Azure Portal, 215Azure Power BI Embedded, 191,

195Azure Protection and Recovery,

215Azure Queue Storage, 170, 172Azure Redis Cache, 181, 190–191Azure Reserved Virtual Machines

Instances, 162, 164Azure Resource Manager (ARM),

198, 215Azure Scheduler, 215Azure Search, 175, 177Azure Security Center, 210–211Azure Security & Compliance,

215Azure Service Fabric, 162, 166,

178, 180Azure Service Health, 215Azure Site Recovery, 171, 174,

215Azure SQL

Database, 181–185,192–199–200, 218

Data Warehouse, 181,185–186, 200

Server, 217Stretch Database, 181, 186

Azure Storage, 170, 171Azure StorSimple, 171, 173–174Azure Stream Analytics, 191,

192–193, 203, 205–206Azure Text Analytics API, 192,

197Azure Time Series Insights, 204,

206

Azure Traffic Manager, 167, 169,215

Azure Virtual Machines (VMs),161–164

Azure Virtual Network, 167, 191Azure VPN Gateway, 167, 168

BBack-end resources, operation

requirement, 136Back-end systems, 152, 207Bar codes, 202BCR. See Benefit-cost ratioBenefit-cost ratio (BCR), 240Bidirectional communication,

possibility, 204Big Data, 47, 52, 181, 203

3-Vs, 172, 194analysis, 41challenges, 191Hadoop framework, 186, 200impact, 54fNIST definition, 16–17open source ecosystem, 55fphilosophy, 218platforms, usage, 173, 194presence, 145query, 196scalable framework, 144–145usage, 248volume/variety/velocity/

variability, 17–18Binary large object (BLOB)

format, 187types, 171

Block Blob, 171Business

insights, improvement, 31productivity, 153–154solution, 229

Business intelligence (BI), 195,205

reporting tool, usage, 174


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CC173DX, 188Capital expenditure, costs (shift),

81CCM. See Cloud Controls MatrixCDN. See Azure Content Delivery

NetworkCegid, 241Central processing unit (CPU)

intensiveness, 152number, decrease, 119usage, 137

Chef (IT platform), 139Chellapa, Ramnath, 44CI/CD. See Continuous

integration and continuousdevelopment

Cisco Global Cloud Index, 78f,80f, 240

Citrix, integration, 211CJIS. See Criminal Justice

Information ServicesClassic Load Balancer, 135Cloud

adoption, impact, 88tapproach, steps, 93fbenefits, 40–41, 81–89, 82f, 83fbusiness reasons,

identification, 94–96challenges, 85f, 87fcharacteristics, 61–70Cisco global cloud index, 78fcloud-managed service,

207–208cloud-native database, 186computing facilities, impact,

155, 157data, 39f, 41deployment models, 77feconomies, impact, 41edge, 205enterprise use, prevention

(factors), 95f

logic, 149management tools, 137–140maturity, increase, 87fprice index, 70fproviders, 40, 109public cloud, private cloud

(contrast), 80freadiness checks, 96–99,

97t–98treadiness results, example, 98fR framework, migration

(example), 100fscalability, 40services, 160–161, 190–191solutions, company adoption

(percentage), 85tsovereign clouds, 77type, identification, 94,

107–113users, costs/benefits, 89fvendor, 107–113, 112f

Cloud Adoption Framework(CAF). See Amazon WebServices

Cloud-based demand-drivensupply chain (CBDDS), 39f,40

Cloud computing (CC), 44economic impact, 95fimpact, 242IT benefits, 87fNIST definition, 61onset, 238time line, 45f–46fusage, prevention (factors),

95fCloud Controls Matrix (CCM),

160Cloudera, 173, 194Cloud migration, 106f

decisions, KPMGconsiderations, 104

factory, 108f, 114f


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Cloud Security Alliance (CSA),160

Cloud service, 145, 192consumer options, 64economic return, ranking, 109tidentification, 107–113integration, 238–239models, 71f, 75fprovider, selection, 110t, 111tpublic providers, EU market

shares, 113ttypes, availability, 109

Cognitive computing, 249Cognitive services, 161, 200–203Cold data, analysis, 41Collaboration, usage, 9, 29–41Collaborative approach, usage,

29–41Columnar store (data storage),

130Column family data model,

example, 189fCommon Criteria EAL4+ security

standards, usage, 211Complex event process (CEP)

pipelines, 193Compliance certification, 187Compute category, 161–167Computing resources, usage, 40Connected devices, usage, 243Connected supply chain

management, 243fConsumer packaged goods

(CPGs), 10Containers, 178–181Content delivery network (CDN),

Amazon provision, 134Content, end-user requests, 134Continuous integration and

continuous development(CI/CD), 164, 179

Conversation bots, 149Cookie-based sessions, 168

Cookie, storage, 168CPGs. See Consumer packaged

goodsCriminal Justice Information

Services (CJIS), 160Cron job syntax, usage, 165Cryptographic keys, storage, 211CSA. See Cloud Security AllianceCustomerID, sequence

(example), 188Customer journey analytics, 249Customer satisfaction,

improvement, 31

DDark Data

avoidance, 251databerg, relationship, 32f

Dark Lake phenomenon,avoidance, 196

Data, 9–21, 41, 246analytics, 109centers, 49f, 53f, 118cloud/advanced analytics,

combination, 39fconsistency, 187creation, 41data-driven analytics, usage,

243data-driven approach, 150data-driven decisions, 248–249dimensions, 218encryption, 173flow, 12f, 195fgeneration, Cisco GCI estimate,

155intelligence, 191–200lakes, 17f, 41leveraging, 41models, 187, 224services scale, organizational

needs, 41sources, 192


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warehouse, MPP architecturedesign (usage), 199–200

Database administrators (DBAs),usage, 129

Databases, 128–133engines, Amazon RDS cloud

service support, 129RDS support, 128

Databerg, dark data(relationship), 32f

Data transaction units (DTUs),183. See also Elastic datatransaction units

DC/OS, 165, 178, 180DDPO. See SAS Demand-Driven

Planning and OptimizationDedicated hosts, 120Dell, 167Demand

demand-driven insights, 41forecasts, usage, 246management, 247planner, interface, 246sensing, 28shaping, 28–29, 29f

Demand-driven forecasting, 202,231

IoT, relationship, 25fsolutions, 246

Demand-driven supply chain(DDSC), 30, 30f, 203

advantages, 31benefits, 40fintegration/technologies, 245f

Demand forecastingAzure Cloud, usage, 216–218data, usage, 17f

Demand signal repository (DSR),36, 223, 224, 228, 235

Deployment models, 75–81Descriptive Diagnostic Predictive

Prescriptive (DDPP) model,21, 22f

Desktop streaming, 154–155Developer tools, 136–137, 161,

213–214DevOps, 104, 182, 214Digital infrastructure, impact,

249Digitalization, 243Digital supply chain,

interconnection, 5fDisaster recovery (DR), 82, 110Discounted unit prices, 68Distributed denial of service

(DDoS) attacks, protectionservices, 143

Distribution planning, 244Docker Hub, 164, 166, 178, 179Docker Swarm, 179–180Document data model, example,

190tDocument store (data storage),

130Domain name system (DNS),

124, 135Downstream consumers,

recommendations, 41DPM. See MicrosoftDR. See Disaster recoveryDrag-and-drop interface, usage,

138DSR. See Demand signal

repositoryDTUs. See Data transaction unitsDynamic capabilities, leverage,

234Dynamic data masking, usage,

184

EEBS. See Amazon Elastic Block

StoreEC2. See Amazon Elastic

Compute CloudEconomic return, 109t


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Economies of scale, 40, 238–239ECR. See Amazon Elastic

Compute Container RegistryECS. See Amazon Elastic

Compute Container RegistryEdge locations, 134EDIFACT, 207eDTUs. See Elastic data

transaction unitsEffective, efficient, and

economical (3-E test),105

EFS. See Amazon Elastic FileSystem

Elastic data transaction units(eDTUs), 183

Elasticity, leverage, 234Elastic pools, usage, 183ELB. See Amazon Elastic Load

BalancingElectronic data interchange

(EDI), support, 207EMR. See Amazon Elastic Map

ReduceEnd-user devices, 154End-user requests, 134Enterprise-grade cloud service,

requirement, 154Enterprise integration, 161,

208–210Enterprise resource planning

(ERP) systems, 195Enterprise SaaS growth, market

leaders, 76fEnterprises, cloud computing

usage (prevention), 95fETL. See Extract, transform, and

loadEuropean Union (EU) cloud

provider market, 111service, 113t, 239

Europe, cloud computing(economic impact), 95f

Evaluation metrics, 229Event-driven applications,

development, 206–207Event stream processing (ESP),

13–14Exponential smoothing, usage,

217ExpressRoute circuit, creation,

170Extract, transform, and load

(ETL) data, usage, 147

FFacebook, 149, 212Facebook Messenger, 193Failover, 169, 184Federal Risk and Authorization

Management Program(FedRAMP), 160, 187

FireOS, mobile application usage,150

First-in, first-out (FIFO)processing, 153

Forecasting, 9, 251demand-driven forecasting, IoT

(relationship), 25fForecast reconciliation, 246Forecast value added, example,

34–35Front-end applications, interface,

152

GGames, development, 158–159GCM. See Google Cloud

MessagingGDPR. See General Data

Protection RegulationGeneral availability (GA), 150General Data Protection

Regulation (GDPR), 196,238, 251

alteration, 38


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launch, 105regulations, impact, 94

Genomics, 200, 201Germany

cloud vendor benchmark(2016), 112f

IT provider, leverage, 241GitHub, 164, 179, 214Git, usage, 178, 214Google, 200, 207, 240Google+, 212Google Chromebooks, 154Google Cloud Messaging (GCM),

178, 207Graph database (data storage),

130Graphics processing units

(GPUs), 152

HH.265 video, conversion, 152Hadoop, 144–145, 192Hadoop framework (HDFS), 173,

186, 194, 200Hard disk drives (HDDs)

memory, usage, 131organization selection, 126selection, 146

Hardware security module(HSM), 142, 173, 194, 211

Hash key, 188Health Information Trust

Alliance (HITRUST), 187Health Insurance Portability and

Accountability Act (HIPAA),160, 187

High-level lambda architecturedesign, 18f

High-performance computing(HPC), 166, 179

HIPAA. See Health InsurancePortability andAccountability Act

HITRUST. See Health InformationTrust Alliance

Hortonworks, 173, 194Hot data, analysis, 41HSM. See Amazon CloudHSM;

Hardware security moduleHTML 5 compatibility, 155Hybrid data flow, supply chain

analytics, 20fHypertext transfer protocol

(HTTP), 165, 168Hyper-V virtual machines. See

Microsoft

IIA. See Industrial analyticsIAM. See Amazon Identity and

Access ManagementIDE. See Integrated development

environmentIdentity, 140–143Image recognition, usage, 202Implementation partners,

availability, 110Industrial analytics (IA), 250Industrial Internet Data Loop,

203, 204fIndustrial Internet economic

potential, 156fIndustrial Internet of Things

(IIoT), 192, 203, 205, 252Information communication

technology (ICT), 81, 249Information, recording, 138Information technology (IT), 2,

44, 81, 118architecture design, ability, 224Big Data, impact, 54fcloud computing, impact, 87foperating model, 238

Infrastructure as a Service (IaaS),70–72, 107, 159, 225, 240

example, 73f


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Infrastructure as a Service (IaaS)(Continued)

platforms, public cloud, 81fvirtualization/usage, 234

In-memory analytics, 226In-memory OLTP, usage, 181Input/output (I/O), usage, 172Inputs, optimization, 247–248Integrated development

environment (IDE), 164,179, 197

IntelliJ IDEA, 197Internal Revenue Service (IRS)

1075, 160International Traffic in Arms

Regulations (ITAR), 160Internet of Things (IoT), 6, 13,

109, 155–158, 192, 194,203–208

demand-driven forecasting,relationship, 25f

demand sensing, example, 28devices, usage, 199earning potential, 155time-series data, 206

Internet Protocol (IP), 124, 138defining, organizational

control, 134IPSec VPNs, support, 168

Inventory, 243costs, reduction, 31optimization, 231–235reduction, possibility, 248–249

iOS, mobile application usage,150, 154, 178, 207, 212–213

iPad, usage, 154iPhone X, Hello feature, 202IRS. See Internal Revenue ServiceISG cloud readiness

check, example, 96, 97t–98tresults, example, 98t

ITAR. See International Traffic inArms Regulations

JJava Database Connectivity

(JDBC), 147JavaScript, 165JavaScript Object Notation

(JSON), 130, 181, 189Java, support, 197Jupyter notebooks, support, 197

KKafka platform, 192Key-value, 130, 187, 188tKMS. See Amazon Key

Management ServiceKPMG, cloud migration

considerations, 104Kubernetes, 165, 180, 181Kundra, Vivek, 44

LLambda architecture design, 18fLatency-based routing,

configuration, 135Lead time, 242, 243Lift and shift, 103–104Lightweight Directory Access

Protocol (LDAP), 141, 213Linux VMs, 164Low-priority VMs, 68Lua scripting language, support,

190

MMachine learning (ML), 41, 147,

200, 203, 226macOS applications, 213, 214MAD. See Mean absolute

deviationManagement, 215–219MAPE. See Mean absolute

percentage errorMapR, 173, 194MariaDB, 128, 129


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Market share/reputation, 110Massive parallel processing

(MPP), 146, 185–186,199–200

Material inventory, reduction,242

Material requirements planning,244

Mean absolute deviation (MAD),223

Mean absolute percentage error(MAPE), 223, 229

Memcached, 131Mesophere, 165, 178, 180, 181Messaging services, 152–153Metadata tagging, 202MHI 2018 survey results, 8fMicroservices-based design,

usage, 104Microsoft (MS), 24f, 25f

Azure. See Azure.Bing, usage, 200cloud, 54, 101, 103–104, 241Exchange, 174Genomics, 200, 201hypervisor virtualization

technology, 174hyper-V virtual machines,

174Office 365, 207Power BI. See Power BI.R Server, integration, 197SharePoint, 174Skype, 193System Center Data Protection

Manager (DPM), 174Teams, 193Visual Studio, 164, 182, 198,

213–214Windows, usage, 154, 178Xbox game console, 159

Microsoft Dynamics, 159Microsoft Office, 159

Microsoft Push NotificationService (MPNS), 178

Microsoft SQL Server, 129, 132,159, 174, 181

databases, 186Stretch Database, 186

Migration, 131–133factory, 94, 113–115, 132

ML. See Amazon MachineLearning; Azure MachineLearning; Machine Learning

Mobile cloud services, 175–178Mobile services, 148–151Monitoring, 215–219Monolithic design, usage, 104MPEG2 video, conversion, 152MPNS. See Microsoft Push

Notification ServiceMPP. See Massive parallel

processingMTCS. See Singapore Multi-Tier

Cloud SecurityMultifactor authentication, 173,

194Multimodel data storage, 130Multimodel support, 187Multitier causal analysis, 246MyDay, integration, 211MySQL, 128, 129

Azure Database, usage, 181,185

NNational Institute of Standards

Technology (NIST)definitions, 17, 61

Natural language understanding(NLU), 148

Netflix, case study, 115Networking, 133–135, 167–170Networks

network-level providedinformation, 135

topology, visualization, 170


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News and media company, casestudy, 222–229

Node.js, 164, 185, 207, 214NoSQL database, 149, 186

management, 206types, evolution, 130

OOAuth 2.0, protocol, 212Oil and energy company, case

study, 230–235OLAP. See Online analytical

processingOmni-channel demand insights,

31On-demand instances, 120On-demand services, 134On-demand video-streaming

services, 152One-time passcode (OTP), 212Online analytical processing

(OLAP), 130, 181Online retailers, websites, 134Online shopping, example, 28–29Online transactional processing

(OLTP), 130, 181On-premises sources, 146–147OpenID Connect, protocol, 212Open source

ecosystem, 55fRedis cache engine, basis, 190

Operating expenditure, costs(shift), 81

Operating systems (OSs), choices,119–120

Operational databases, 41Oracle, 128, 129, 240Oracle to Oracle, 132OTP. See One-time passcode

PPage Blob, 171Parallel computing, 234

Parallel workloads, usage, 166Passwords, storage, 211Pay-as-you-go (PAYG), 239

business model, 120cost model, 68, 134financial model, 36, 41, 229model, 164priority, 179

Payment Card Industry (PCI),187

People, collaborative approach(usage), 29–41

Performance tests, settings, 228Performant platform, 206,

248–249Personalized recommendations,

28–29, 29fPHP, 164, 179, 207Platform as a service (PaaS), 40,

71–74, 107, 128example, 74fusage, 159, 164, 225, 240

Point-in-time restore (availabilityfeature), 184

Point of sale (POS) scanners,usage, 199, 202, 208

POSIX-based access control lists(ACLs), 173, 194

PostgreSQL, 36, 128, 129Azure Database, usage, 181,

185Power BI, 195, 197, 217, 2001PowerShell, 165, 178, 214Price-to-value ratio, 110Private cloud, public cloud

(contrast), 80fProcesses, collaborative approach

(usage), 29–41Product availability, increase, 242Product dimension hierarchy, 10fProduct inventory, reduction,

242Production capacity, 243


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Product management, 227, 234Public cloud, 81f, 238–239

AWS global regions, 125fleadership, race, 112fprivate cloud, contrast, 80f

Push and pull, 4Push notifications, 149Python, 164, 167, 179, 185, 193,

206, 214

QQlikView, 197QR codes, 202Query processing unit (QPU),

198

RR (platform), 192, 206Radius, 213Random-access memory (RAM),

amount (decrease), 119Random walk, forecast

(example), 34fRDS. See Amazon Relational

Database ServiceReal-time analytics, usage, 169Recovery point objective (RPO),

182Redhat, usage, 120Redis, 131Relational databases, 130Representational State Transfer

(REST)APIs, usage, 172, 178, 214web protocol, 171

Research and development(R&D), 227, 234

Reserved instances (RI), 68, 120,164, 179, 239

REST. See Representational StateTransfer

Retainable evaluator executionframework (REEF), 59

Retire Replace Retain and wrapRe-host Re-envision (5Rs),101, 103–104

Retire Retain Re-hostRe-platform Re-purchaseRe-factor (6 Rs), 100–101,102f

Revenue growth, cloud adoption(impact), 88t, 239

R framework, 99–107, 100fRLS. See Row-level securityR-model, 92, 94, 99–100, 107,

241Rockwell Automation, cloud

service leverage, 204Row-based storage, usage, 182Row-level security (RLS),

184–185RPO. See Recovery point

objectiveR Server, integration, 197

SS3. See Amazon Simple Storage

ServiceSaaS. See Software as a serviceSales

opportunities, increase, 31random walk, forecast

(example), 35frevenue, increase, 31

Sales and operations planning(S&OP) processes, 240

Sales and operations process, 4fSalesforce (SFDC), 207SAML. See Security Assertion

Markup Language 2.0SAP, 241

ASE, 132Business Objects Lumira, 197enterprise resource planning

(ERP), 195SAS, 36, 245


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SAS Demand-Driven Planningand Optimization (DDPO),37f

software solution suite, 36Scalability, 40, 82Scala, support, 197SCCTs. See Supply chain control

towersSchema, defining, 141Schema-on-write approach,

relational database usage,130

SDK. See Software DevelopmentKit

Seasonal random walk, forecast(example), 35f

Secure socket layer (SSL)leverage, 129, 142offloading, 168

Security, 140–143Security Assertion Markup

Language 2.0 (SAML 2.0),211, 212

Server Message Block (SMB)protocol, 172

Server Migration Service (SMS),131, 132, 212–213

Servers, virtualization, 48fServer virtual machine, 228Service

levels, 31, 110models, 70–75tiers, 198type, identification, 94

Service-level agreements (SLAs),105, 120, 182, 194

absence, 191Service Organization Control

(SOC), 160SES. See Amazon Simple Email

ServiceShared responsibility model, 71

Singapore Multi-Tier CloudSecurity (MTSC), 160

Single sign-on (SSO), 173, 179,194

SmartFocus, 241SMS. See Amazon Web ServicesSNS. See Amazon Simple

Notification ServiceSOC. See Service Organization

ControlSoftware as a service (SaaS), 40,

71, 107, 159, 225, 240applications, 26, 103consumer options, 64growth, market leaders, 76fservice models, 70

Software-defined data centers(SDDCs), 71, 76, 238

Software Development Kit(SDK), 138, 213

Software tool/components,55–61

Software vendor, strategicpartnership, 227

Solid state disk (SSD)memory, usage, 131selection, 126, 146storage, 124throughput provision, 172

S&OP. See Sales and operationsplanning

Sovereign clouds, 77Spark platform, 192Spark Streaming, 196Speech recognition, 202Spot instances (AWS), 68, 120SQL. See Structured Query

LanguageSQL Server Analysis Services

(SSAS), 198SQS. See Amazon Simple Queue

Service


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I N D E X 309

SSAS. See SQL Server AnalysisServices

Star schema, forecastdimensions, 12f

Static Internet Protocol (IP)address, 124

Storage, 109, 125–128,170–174

Storm platform, 192Strategic supply network

planning, 244Structured Query Language

(SQL), 57, 144, 146Management Studio, usage,

182Server Data Tools, usage,

198U-SQL, 193

Supply chainagility, enhancement, 31analytics, 12f, 20fbusiness, 224control tower, 7fdemand-driven supply chain

(DDSC), 30, 30f, 40fmanagement, 242, 243f,

247–248, 251foptimization, 232f, 244–245performance, improvement,

247technologies, 250f

Supply chain control towers(SCCTs), 6–8

SUSE, usage, 120SWF. See Amazon Simple

WorkflowSystem integrators (SIs), usage,

242

TTableau, 197Tabular Model Scripting

Language (TMSL), 198

Tabular Object Model (TOM),SSAS support, 198

Tasks, automation/outsourcing,82

TDE. See Transparent dataencryption

Temporal logic, implementation,205

Tesco, Big Data/advancedanalytics (usage), 248

Text-to-voice functionality, 147Third-party networking,

leverage, 167Throughput times, 242, 243ThyssenKrupp, cloud service

leverage, 204Time efficiencies, 82Time horizon, defining, 228Time stamp, 138TMSL. See Tabular Model

Scripting LanguageTOM. See Tabular Object ModelTraffic routing methods, 169Transactional data, usage,

183–184Transcoding processes, CPU

intensiveness, 152Transparent data encryption

(TDE), 184Transport layer security (TLS),

leverage, 142T-Systems, 241Twitter (identity provider), 149Two-tier deployment

architecture, 228

UUbuntu, usage, 120UK G-Cloud, 160Uniform resource identifier

(URI), 131Unique selling point (USP), 205Unit4, 241


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UPS, Azure/bots/cognitiveservices usage, 193

Uptime, cloud vendor selectionfactor, 110

User authentication/management, 150

User data storage, 149User sign-in, 149USP. See Unique selling pointU-SQL, 193

VValue-added chain,

plan/management, 244Variability, 18Variety, 17Velocity, 18Vending machine, IoT demand

sensing (example), 28Vendor

ecosystem, 110knowledge/experience/

know-how, 110lock-in, 86market comparison, 111ttype, identification, 94

Video streaming services, 134Virtual CPUs/RAM, performance

baseline, 120Virtual desktop infrastructure

(VDI) solutions, 154Virtual infrastructure, 138Virtualization, 47–52, 174Virtual local area networks

(vLANs), 135Virtual machines (VMs), 138. See

also Azure Virtual Machines;Linux VMs; Low-priorityVMs; Microsoft; WindowsVMs

containers, comparison, 50f

form, 119low-priority VMs, 68operating system, 47–48running, 168shutdown, 120types, 163usage, 103–104VMware-based virtual

machines, 174Virtual private networks (VPNs),

134, 213leverage, 129tunnel, 167

vLANs. See Virtual local areanetworks

VMs. See Virtual machines(VMs)

VMware, usage, 47, 174VMware vCloud Air, 240Voice/image recognition, 147Volume, 17Volume Variety Velocity

Variability (4 Vs), 17–18VPC. See Amazon Virtual Private

CloudVPNs. See Virtual private

networks

WWAF. See Amazon Web

Application FirewallWeb cloud services, 175–178Weighted round robin, 135,

169Windows Push Notification

Service (WNS), 178, 207Windows Store, technology

leverage, 187Windows VMs, 164WNS. See Windows Push

Notification Service


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Wrappers, addition, 103WS-Federation, 211

XX12, 207x86 hardware, virtualization,

238Xamarin, 214

Xbox Live gaming services,technology leverage,187

XML data, usage, 181

YYet another resource negotiator

(YARN), 193


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Documents

Index []€¦ · Amazon Microsoft SQL Server instances, 142 Amazon Mobile Analytics, 149–151 Amazon Mobile Hub, 149 Amazon Mobile SDK, 149, 150 Amazon OpsWorks, 137, 139 Amazon