Hvorfor migrere til Cloud? - Visma · 2019-11-29 · Lab Architecture SynapseDataFactory-suffix...
Transcript of Hvorfor migrere til Cloud? - Visma · 2019-11-29 · Lab Architecture SynapseDataFactory-suffix...
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Hvorfor migrere
til Cloud?Jon Jahren, Microsoft Norge
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Why move to the cloud?
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Why move to the cloud?
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Why move to the cloud?
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Why move to the cloud?
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Why move to the cloud?
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Photo: Iwan Baan/New York Magazine
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Why move to the cloud?
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Business Returns
IT must rapidly produce measurable
business returns to stay relevant
Transformation
Evolving how businesses operate and
interact with the market
Modernization
Improving customer and employee
experiences
Business Transformation enabled by Cloud Technologies
Key Business Drivers
Growth
Scaling products and services to meet
ever growing business needs
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The major drivers for
IT Governance
Keep risk at acceptable levels
Maintain availability to systems
and services
Consistently apply policy and
audit compliance
Protect customer data
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Objective of this model: Create balance
Control &
Stability
Speed &
Results
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Organisations are Built for Yesterdays Market
M2: Speed, Agility & Sensing
M1: Todays Operational Reality
12-18 months
Mode 2: Built to Evolve Dynamic marketplace with increasing frequency of disruptive innovations –requiring a rapidly agile and adaptive operating model
Mode 1: Built to Run Market stability – multi year\decade innovation cycles – requiring an operating model that captures markets and extracts value
… Transformation is a Bimodal Challenge
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Digital Transformation
One story two entry points
Mode 1Mode 2
Real-time Brand
Experience Challenge
Digital Customer
Dialogue
Systems of
Engagement
Systems of
Intelligence
Turn data into Bus
Model insight
Agile
M2: IT Ops Model
- Low cost & low change risk
- On demand – agile cycle times
- Distributed empowerment
- Zero based infrastructure
M1: IT Ops Model
- High cost & high change risk
- Waterfall cycle times
- Centralised delivery
- Infrastructure friction
Business Led IT Led
COGS – Customer Value &
Revenue Focus
BLUE OCEAN RED OCEAN
SG&A – Cost reduction strategy –
DT funding challenge
Digital Platform Adoption
Shifting complexity, integration &
lifecycle risk to the platform provider
Managing Complexity
Best of breed complexity, integration &
lifecycle risk borne by Enterprise IT Model
Systems of Record – Years of
accumulated technical debt
- 20% Growth Assets
- 80% Cost Reduction Directive
- Untapped M1 data
Outsourcing Model:
Underwrite the OpEx Savings
with 5 yr contract – your mess
locked in, for less
Cannot get to M2 with outsourcing
model – Direct to Vendor
Customer
Service
Revolution
Digital
Workforce
Revolution
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Microsoft Cloud Adoption Framework for Azure
Ready
• Azure readiness guide
• First landing zone
• Expand the blueprint
• Best practice Validation
Plan
• Digital estate
• Initial organization alignment
• Skills readiness plan
• Cloud adoption plan
AdoptDefine Strategy
• Understand motivations
• Business outcomes
• Business justification
• Prioritize project
ManageBusiness commitmentsoperations baseline •
Ops maturity
GovernMethodology • Benchmark
initial best practice • Governance maturity
Migrate• First workload migration
• Expanded scenarios
• Best practice validation
• Process improvements
Innovate• Innovation guide
• Expanded scenarios
• Best practice validation
• Process improvements
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Assess current state and future state to
establish a vision for applying the frameworkAssess2
Establish a Minimally Viable Product (MVP) to
serve as a foundation for governanceMVP3
How do I get started?
Frame the conversation to mitigate tangible
business risks through consistent governanceFramework1
Mature with each release to align Cloud
Adoption and existing IT functionsEvolve4
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Traditional Approach to Control
Block Dev/Ops from directly accessing the cloud (portal/api/cli) to attain control
Developers
Operations
Cloud Engineers responsible for Cloud environment
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Speed + Control
Cloud Engineers
Developers
Operations
Management
Groups
Templates RBAC
Blueprints
Policies
Policy
Cloud-native governance -> removing barriers to compliance and enabling velocity
Cost
Management
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Assess current state and future state to
establish a vision for applying the frameworkAssess2
Establish a Minimally Viable Product (MVP) to
serve as a foundation for governanceMVP3
How do I get started?
Frame the conversation to mitigate tangible
business risks through consistent governanceFramework1
Mature with each release to align Cloud
Adoption and existing IT functionsEvolve4
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Understand the business vision driving cloud adoption
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Evaluating current state
Security managementappears to be an important area of focus for this customer.
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Assess current state and future state to
establish a vision for applying the frameworkAssess2
Establish a Minimally Viable Product (MVP) to
serve as a foundation for governanceMVP3
How do I get started?
Frame the conversation to mitigate tangible
business risks through consistent governanceFramework1
Mature with each release to align Cloud
Adoption and existing IT functionsEvolve4
![Page 27: Hvorfor migrere til Cloud? - Visma · 2019-11-29 · Lab Architecture SynapseDataFactory-suffix Azure Data Factory Lab 1: Load Data into Azure Synapse Analytics using Azure Data Factory](https://reader034.fdocuments.us/reader034/viewer/2022042221/5ec800fb4118917f3065b726/html5/thumbnails/27.jpg)
2. Subscriptions: To group similar
resources into logical collections
3. Resource Groups: To further group
applications or workloads into
deployment and operations units
1. Management Groups:
To reflect security,
operations and
business/accounting
hierarchies
Resource OrganizationThe basic foundation of all governance practices
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Assess current state and future state to
establish a vision for applying the frameworkAssess2
Establish a Minimally Viable Product (MVP) to
serve as a foundation for governanceMVP3
How do I get started?
Frame the conversation to mitigate tangible
business risks through consistent governanceFramework1
Mature with each release to align Cloud
Adoption and existing IT functionsEvolve4
![Page 29: Hvorfor migrere til Cloud? - Visma · 2019-11-29 · Lab Architecture SynapseDataFactory-suffix Azure Data Factory Lab 1: Load Data into Azure Synapse Analytics using Azure Data Factory](https://reader034.fdocuments.us/reader034/viewer/2022042221/5ec800fb4118917f3065b726/html5/thumbnails/29.jpg)
10s - 1,000s of apps Diverse infrastructure Multi-cloud
IoT devices Edge
Datacenters
Branch offices
Hosters
OEM hardware
VMs
Containers
Databases
Serverless
Customer environments are increasingly complex
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Microsoft Azure
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Always current Cloud billingElastic scale Unmatched securityUnified management
• Deploy in seconds
• Scale up/down, scale out
based on capacity
• Automation of DB tasks at
scale, e.g. high availability
• Access to Azure Advanced
Data Security
• Consistent governance with
Azure Policy & Role-based
Access Control
• Modern cloud billing
model across hybrid
infrastructure
• Leverage existing licensing
investments with Microsoft
• Single view across on-
prem and public cloud
• Integrated management
across data infrastructure
and operations
Azure data services anywhere
Any hardware, any Kubernetes
Bring Azure data services to any infrastructure with Azure Arc
• Automated patching/updates
based on customer policy
• Hyperscale innovation now
available on-premises
• Evergreen SQL with no end-
of-support
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Apps and BICustom
apps AnalyticsBI
AKS
…
Any Kubernetes
Any hardware
Azure data services
Microsoft Azure
Site Recovery
OEM hardware
Azure Arc management
GKE EKSKubernetes OPENSHIFT
Azure Site RecoveryMonitoringAzure Security
Provisioning
HA/DR
Scaling
Updates
Backup
Diagnostics
Azure data services anywhere at a glance
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© Microsoft Corporation
Modern Data Platform Reference Architecture
Semi-StructuredV=Volume
csv, logs, json, xml(loosely-typed)
Non-structuredV=Variety
images, video, audio, free text(no structure)
StreamV=Velocity
IoT devices, sensors, gadgets(loosely-typed)
Load and Ingest
Store
Process
Serve
Data Factory
Event Hubs Stream Analytics
Relational Databases(strongly-typed, structured) Azure Synapse Analytics Power BI Premium
DatabricksAzure Data Lake Gen2 CosmosDB
Power BI Premium
Application
Analytics
Analytics
λ Lambda Architecture
Hot PathReal Time Analytics
Cold PathHistory and
Trend Analysis
Stream Datasets
and Real-time
Dashboards
Enterprise-grade
semantic model
Integrate big data
scenarios with
traditional data
warehouse
Cognitive ServicesAzure ML
Business User
Fast load
data with
Polybase/
ParquetDirect
Build and Score ML models
Scheduled / event-
triggered data ingestion
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© Microsoft Corporation
Azure Data PlatformResource Group
ADPDesktopVirtual Machine
ADPVirtualNetworkVirtual Network
MDWResourcesStorage Account
ADPLogicAppLogic App
SynapseDataLakesuffixAzure Data Lake Storage Gen2
ADPEventHubs-suffixEvent Hubs
SynapseStreamAnalytics-suffixEvent Hubs
SynapseDataFactory-suffixAzure Data Factory
ADPDatabricksAzure Databricks
ADPComputerVisionComputer Vision API
ADPCosmosDB-suffixAzure CosmosDB
PowerBIPower BI Desktop/Workspace
Azure Data Platform End2EndLab Architecture
SynapseDataFactory-suffixAzure Data Factory
Lab 1: Load Data into Azure Synapse Analytics using Azure Data Factory Pipelines
Lab 2: Transform Big Data using Azure Data Factory Mapping Data Flows
Lab 3: Explore Big Data with Azure Databricks
Lab 4: Add AI to your Big Data pipeline with Cognitive Services
Lab 5: Ingest and Analyse Real-Time Data with Event Hubs and Stream Analytics
1
2
3
4
1
1
2
2
3
4
5 6
1
1
2
3
3
4
Student s Computer
operationalsql-suffix\NYCDataSetsAzure SQL Database
2
synapsesql-suffix\SynapseDWAzure Synapse Analytics
5
4
RDP Connection or
Azure Bastion
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PAGE 39
Example Reference Architecture for Data & Analytics
Raw Data Enriched data
Valu
e A
dd
ed
IT
Serv
ices
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Example Data Journey
Enterprise
Data
Warehouse
Federated BI
(Logical DW)
Self-service BI
Enterprise
Information
Management
Transformation:
Data &
Intelligence
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PAGE 41
Data Strategy Measures of Success
41
Data Foundation
• Modern Architecture
Usage
• Data Health
Scorecards
• Active Usage (View +
Go-to-Green plan)
• # of business issues/
customer metrics
moved from red to
green
Diagnostics
• Culture: # of
experiments
• Impact of Diagnostics
($$/ Satisfaction)
ML & AI
• Culture: # of AI/ ML
Models
• Impact of ML/ AI ($$/
Satisfaction)
Raise the Overall Data Quotient!
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Example Approach to the Data Lake
o Get Executive Sponsorshipo Create architect/Engineer and COEo Agree Basic Tenets and data lake approacho Define standards & governance model
Run PoC Create infrastructure Socialize
Identify Key scenario Implement governanceRegular updates to
leadership team
Create initial lake Implement infrastructure
Working group socializes each department
Complete end-to-end scenario
Add telemetry, tagging etc.Expectation setting to
business
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Data Management
Tenets
43
Create Agility by moving to API access
Reduce Cost & Risk
Minimize Data Duplication – Compute to storage
Production data governed with high quality
Deliver Data at the Right Time
Modernization
Elastic Scale out of storage & compute separately
Discover & Publish single source of truth data
Include ML into initial solution design
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Example Data Platform : Architecture
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PAGE 45
Drilldown: Data Vision Guiding Principles
Focus on horizonal/strategic capabilitiesStrategic projects with higher impact across org
Business/ Customer driven approach
Driving the Culture with Change Leadership
Strategic outside-in approach (user view)
Quad Model Approach for Product Development
Drive Change Leadership with data everyday
Build on Connected capabilitiesCreate unique connected data sets
Standardize access requirements
One Data TeamUtilize the full-stack capabilities
Up the Data Quotient from Foundation to ML/AI capabilities
Keep and double down on what works
Data platform vision execution
Productized ML
Scorecard & Service Scorecards
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