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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
COPYRIG
HTED M
ATERIAL
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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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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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