1. Modern data architecture at scale in the cloud : Best practices of Serverless, lambda and microservices architecture
Prakriteswar Santikary, PhD
Vice President and Global Chief Data Officer

2. ABOUT ERT
Founded in 1977; privately held
Supporting Pharmacos, Biotechs & CROs around the world
Operations in 12 countries
2500+ employees
MINIMIZING RISK & UNCERTAINTY, SO YOU CAN MOVE AHEAD QUICKLY WITH CONFIDENCE
│ © Copyright ERT 2018

3. Agenda 01 Definition Characteristics Advantages Disadvantages
Monolithic Architecture
Microservices Architecture
Serverless Architecture
Definition
Characteristics
Advantages
Disadvantages
Definition
Characteristics
Advantages
Disadvantages
Definition
Characteristics
Advantages
Disadvantages
Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate.
Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research
Audience Questions:
How many of you currently employ mHealth technologies in your clinical trials?
How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials?
How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices?
Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research.
Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights
| Copyright ERT 2018

4. Monolithic Architecture – a refresher
TIPS ; Fresh business idea, needs quicker turn around, small engineering team – start with monolith – build simple and lightweight application

5. Monolithic Architecture – Weaknesses and challenges

6. Software Complexity has been growing steadily01
World of Big Data 02
DevOps and Automation
Small incremental release
Continuous deployment
Continuous delivery
Quick time to market
Competitive pressure
Multiple data sources
Multiple channels
Multiple data types
Scale requirements
Real time insight
Big Data (unstructured)
Forces behind
Microservices
Architecture 04
Cloud/Serverless computing 03
Emerging Technology
New Programming language
New Programming framework
NoSQL databases
API-first design
Cloud-first design
Focus on code, not on infrastructure – less IT
Serverless deployment
Pay per use
Cost reduction
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7. Monolith to Microservices Transition – loosely coupled, independent services communicating via lightweight protocol
TIPS : Microservices architecture is not a silver bullet for a bad software design. Design discipline must be exercised as to when to use monolith Vs Microservices.

8. Monolith is hard to scale; Microservices enable y-axis scaling by breaking the application into smaller independent services. Each service is indepedently scaled based on need, enabling horizonal scaling.

9. Microservices Logical Architecture

10. Let’s take a n-tier e-commerce application to explain monolith and microservices

11. Break it down into microservices
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12. Microservices architecture – a process view
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13. Microservices Architecture- Advantages
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14. Microservices Architecture- DisAdvantages
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15. Microservices architecture Design patterns01
Decomposition Pattern 02
Deployment Pattern
Service per host/VM
Multiple services per host/VM
Serverless Deployment
On-premise
Hybrid (with Cloud)
By Business Capability
By business domain
Single responsibility principle
Independently deployable
Small service/small team
You build it, you own it
Domain-driven design
Microservices
Best
Practice 04
Monitoring/Tracing Pattern 03
Data Management Pattern
Polyglot persistence
Database per service
Data consistency
Event sourcing
Eventual consistency
CAP theorem
Log Aggregation
Audit logging
Distributed Tracing
Exception Tracking
Health-check API
│ © Copyright ERT 2018

16. Microservices architecture – best practices
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17. Microservices Architecture – a summary view
Characteristics
Benefits
Challenges
Decentralized
Independent
Do one thing well
Polyglot
API-first Design
You build it; You own it
Agility
Innovation
Quality
Scalability
Availability
Distributed systems
Monolith->Microservices transition not easy
Organizational issues (DevOps)
Skillsets
TIPS : Microservices is not a silver bullet. You still need to follow sound software design principles.
Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate.
Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research
Audience Questions:
How many of you currently employ mHealth technologies in your clinical trials?
How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials?
How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices?
Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research.
Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights
| Copyright ERT 2018

18. Serverless Architecture Focus on your application, not on infrastructure
Characteristics
Benefits
Challenges
No server to maintain
No server to provision
Focus on code
Ephemeral; Stateless
Event-driven
Horizontal scaling (auto)
Pay per use; no idle time
Faster innovation
Lower operational cost
Reduce operational management
Speed to market
Fosters innovation
Skillsets
Vendor lock-in concerns
Lack of operational tools
Architectural complexity
Restriction on execution time
Restriction on memory
TIPS : Build scalable platform and applications using a combination of serverless and microservices in the cloud
Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate.
Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research
Audience Questions:
How many of you currently employ mHealth technologies in your clinical trials?
How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials?
How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices?
Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research.
Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights
| Copyright ERT 2018

19. SERVERLESS ARCHITECTURE – FEW EXAMPLES
| Confidential – Internal Use Only

20. SERVERLESS ARCHITECTURE USING MICROSERVICES
| Confidential – Internal Use Only

21. Lambda Architecture – execution of real time and batch together
Characteristics
Benefits
Challenges
Real time data processing
Batch data processing
Merging of real time and batch in single architecture
Architectural complexity
skillsets
Serve both real and batch use cases simultaneously
TIPS : One architecture supports both real time and batch
Here are a few key learnings we hope to provide with this presentation that may inspire these organizations to embrace this change as an opportunity to innovate.
Paradigm Shift: Companies embarking on new innovation initiatives to determine feasibility of mHealth solutions for clinical research
Audience Questions:
How many of you currently employ mHealth technologies in your clinical trials?
How may of you currently utilize Actigraphy and/or Continous Monitoring devices in your clinical trials?
How many of you work in organizations that are now tasked with POCs or pilots using mHealth devices?
Data Optimization: Enhancing clinical research with mHealth data requires new techniques against non-conventional sources. mHealth data alone does not enhance clinical research.
Practical Applications: Applied use of mHealth in select therapeutic areas in combination with other clinical source data and/or ways where mHealth data may be combined for more visual insights
| Copyright ERT 2018

22. Lambda architecture- Physical view on aws (Serverless)
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23. KEY TAKEAWAYS FROM TODAY’S DISCUSSION
Monolithic architecture
Simple application; quick turn around – good for simple lightweight application – validate ideas quickly. Not ideal for complex and growing applications – scalability is the biggest issue.
Microservices
Complex application; scalability need is high; strong engineering skillsets available; distributed computing is complex; Microservices is no silver bullet – still depends on sound software design.
Serverless Architecture
Cloud-native; no HW to maintain; focus on code, not on infrastructure; works well with microservices;
│ © Copyright ERT 2018

24. Questions?
Prakriteswar Santikary, PhD ? …