London, July 6th 2017

1. www.mongodb.com
MongoDB London
July 5th 2017
Cloud Data Strategy

2. Why cloud means your data strategy needs to adapt
Daniel Barrett
VP UK and Ireland, MongoDB
Daniel.Barrett@mongodb.com

3. Multi-model Built for mission critical workloads
Adoption & Growth 3000+ Enterprise Customers 30k+ Atlas Clusters
Scalable and Resilient
Who Is MongoDB?

4. 40%
6%
30%
23%
Align operational expenditure with value
Scale to new geographies and markets
Lack the required in-house expertise
Reduce time to market and faster app delivery
“What is your primary reason for using cloud
technologies?”
While cost was once widely-cited as the driver of cloud adoption,
time to market has taken its place.

5. Key decision criteria
Deployment Flexibility
On-premise, Private, Public, or Hybrid
without vendor lock-in
Reducing Complexity
Broad use case applicability to avoid
additional complexity
Agility
Accelerate time to market and speed
of change for the business
Resiliency
Engineered for high availability
across distributed architectures
Scalability
Elastically grow with demand
Cost
Aligned to actual demand and value but
with predictability
Security
Leverage best in class and appropriate
security controls
What to think about when choosing a cloud data platform

6. • Bestpracticesinre-platformingtotheCloud
• CustomerStory:IHSMarkit
• CustomerStory:HMRC
• Executingonaclouddatastrategy
• Q&A&Lunch
Agenda

7. Best practices in replatforming to the Cloud
Roman Gruhn
Director Information Strategy, MongoDB

8. Key decision criteria
Deployment Flexibility
On-premise, Private, Public, or Hybrid
without vendor lock-in
Reducing Complexity
Broad use case applicability to avoid
additional complexity
Agility
Accelerate time to market and speed
of change for the business
Resiliency
Engineered for high availability
across distributed architectures
Scalability
Elastically grow with demand
Cost
Aligned to actual demand and value but
with predictability
Security
Leverage best in class and appropriate
security controls
What to think about when choosing a cloud data platform

9. Agility
• Flexible Data Model
• Native Drivers for Major Languages
• Continuous Delivery/Integration
• Ease of application development
Morphia
MEAN Stack
{
first_name: ‘Paul’,
surname: ‘Miller’,
city: ‘London’,
location: [45.123,47.232],
cars: [
{ model: ‘Bentley’,
year: 1973,
value: 100000, … },
{ model: ‘Rolls Royce’,
year: 1965,
value: 330000, … }
]
}
RDBMS

10. Resiliency
• Replica set – 2 to 50 copies
• Replica sets make up a self-healing ‘shard’
• Built for distributed architectures
• Replica sets address:
• High availability
• Data durability, consistency
• Maintenance (e.g. HW swaps, upgrades)
• Disaster Recovery
Application
Driver
Primary
Secondary
Secondary
Replication

11. Scalability
• Increase or decrease capacity as you go
• Automatic load balancing
• Three types of sharding
• Hash-based
• Range-based
• Tag-aware
Shard 1 Shard 2 Shard 3 Shard N
Horizontally Scalable

12. Security
• Strong technical controls, e.g. authentication,
authorization, auditing, encryption, …
• Organisation & Processes, e.g. segregation of
duties, views, redaction, …
• Compliance & Regulations, e.g. data sovereignty,
data lineage, data privacy, HIPAA, …

13. Cloud ManagerOps Manager MongoDB Atlas
Private Cloud or On-Prem Cloud hosted, self managed Public DBaaS: Fully Managed
Same Code Base, Same API, Same Management UI
Deployment Flexibility

14. • Migrate existing database deployments
with minimal impact to your applications:
• Performs a data copy between the source
database and the target database
• Then syncs live data between the source database
and the target database, continuously
• Notifies you when now eligible to switchover
Zerodowntimemigrationtocloud(&backagainifneeded)
MongoDB Live Migration

15. Reducing Complexity Through Multi-Model / Workload
Document
Rich JSON Data Structures
Flexible Schema
Global Scale
Relational
Left-Outer Join
Views
Schema Validation
Key/Value
Horizontal Scale
In-Memory
Search
Text Search
Multiple Languages
Faceted Search
Binaries
Files & Metadata
Encrypted
Graph
Graph & Hierarchical
Recursive Lookups
Geospatial
GeoJSON
2D & 2D-Sphere

16. Take Away
• Evolve and embrace Forward Thinking
Architectures
• Carefully consider all decision drivers
when developing a Cloud Data Strategy

17. Executing on a Cloud Data Strategy
Mat Keep
Director, Product & Market Analysis, MongoDB
Mat.keep@mongodb.com
@matkeep

18. Mainframe Client-Server Web
Control & Efficiency AGILITY & INNOVATION
The Cloud Era
Platform Transformation
Cloud & Mobile
Distributed,
NoSQL Databases
1980s Mid 90s – 2000s 2015>1960s-70s

19. 70
Seismic Shifts — Organizational

20. 71
Seismic Shifts — Application Architecture

21. 72
API Access Layer
Operational Data
Customers
Products
Accounts
ML Models
Shared Physical Infrastructure
App1 App2 App3
1. Development agility
– UI for self-service provisioning & scaling
2. Data Re-use
– Each service’s data is physically isolated into its own
database instance
– Federated across services with appropriate
permissioning
3. Corporate Governance
– Logically managed as one service
Cloud Data Strategy
Standardized, On-Demand Database Service
Cloud Agnostic
Any Cloud, Any Where

22. Cloud ManagerOps Manager MongoDB Atlas
Private DBaaS: On-
Prem
Eliminating Lock-In
Hybrid DBaaS
Public DBaaS: Fully
Managed
Same Code Base, Same API, Same Management UI

23. Private DBaaS

24. 10-Step Methodology for Private DBaaS
Step 1:
Common Workload
Reqs
Step 4:
Enabling Multitenancy
Step 5:
Security Enforcement
Step 6:
Performance &
Uptime SLAs
Step 7:
Mgmt &
Orchestration
Step 8:
Cost Accounting &
Chargeback
Step 3:
Virtualization
Strategy
Step 2:
Hardware & OS
Selection
Step 9:
Implementation
Review
Step 10:
Delivery Model
Discovery
Design
Deploy

25. 76
MongoDB Ops Manager
Management Platform for Private DBaaS
Monitoring
• Deploy, resize, and
upgrade your
deployments with
just a few clicks
• RESTful API to
integrate with your
enterprise
orchestration tools
• Allocate and create
pre-provisioned
server pools; Cloud
Foundry Integration
Automation Backup
• Continuous
backups to
minimize your
exposure to data
loss
• Restore to precisely
the moment you
need with point-in-
time recovery
• Dozens of charts
tracking key
performance
indicators
• Custom alerts that
trigger when key
metrics are out of
range
• RESTful API to
integrate with your
existing APM tools

26. From Traditional to DBaaS
• Slow to build and launch new
applications
• Multiple copies of data
• Complex data reconciliation
controls
• High licensing costs
• Sprawling server estate
12,000+ RDBMS Instances
3,500 Systems, 40,000 Cores
1,200+ Coherence Instances

27. 78
Data Fabric
ü Multi-tenant PaaS
ü Exposing APIs for data streaming and storage
ü Cloud native, self-service
ü Modern, industry standard, open source
technologies
ü Intra-day releases
ü Multi-data center
“Data Fabric provides data storage, query and distribution as a service,
enabling application developers to concentrate on business functionality.”
Data Fabric Clients
Java, .NET, REST
API Layer
CRUD & Streaming
App Server Layer
Java + Linux
Database
MongoDB
Messaging
Kakfa
Security
Authentication&AuditData Fabric

28. 79
Results
• £m license cost avoidance (Coherence)
• Plans to decommission hundreds of servers
• Coherence
• Oracle/SQL databases
Cost Reduction
• 2 foundational applications refactored off Coherence
• Supporting data needs of a dozen applicationsSimplification
• Velocity: Develop new applications in days
• No need for database administration
• self-service data service
• Promotes collaboration and data sharing
Velocity
Slide taken from https://www.mongodb.com/presentations/mongodb-days-uk-building-an-enterprise-data-fabric-at-royal-bank-of-scotland-with-
mongodb

29. Public DBaaS

30. 81
MongoDB Atlas
Managed Database as a Service for MongoDB
• Run for You
• Resilient & Secure
• Multi-Cloud

31. 82
Secure out of the box
Scale your Impact with MongoDB Atlas
Comprehensive
monitoring
Fully managed backups
Self-service &
automated
Highly available by
default
Fully elastic
The latest database
features
MongoDB Atlas is …
MongoDB Atlas includes …

32. 83
MongoDB Atlas Powering Microservices
Architecture
Biotechnology giant uses MongoDB Atlas to allow their customers to execute
& track experiments from any mobile device
Problem Why MongoDB ResultsProblem Solution Results
Over 35 different apps accessed by 10,000+
unique customers on AWS
Each experiment produces millions of “rows”
of data, which led to suboptimal performance
with incumbent databases
RDS & Aurora slow, added code complexity
DynamoDB limited query functionality &
expensive
MongoDB Atlas managed database
service
Flexible document model allows storage
of multi-structured data
Expressive query language and secondary
indexes allow ad-hoc analysis of
experiment data
Scalability to handle growing data
volumes
Thermo Fisher customers now obtain real-
time insights from mass spectrometry
experiments from any mobile device or
browser; not possible before
Improved developer productivity with 40x less
code, improved performance by 6x
Easy migration process & zero downtime.
Testing to production in under 2 months

33. MongoDB Stitch
Fully Managed Backend-as-a-Service
Stitch
Integrated services and pipelines for
complex, multi-stage workflows
Native SDKs for Android, JS, and iOS clients
Direct database access

34. Wrapping Up

35. Conclusion
1 Cloud data strategy is essential for
digital transformation
2 It’s more than just a new platform:
devops, microservices, CI/CD
enable new ways of delivering apps
faster
3 MongoDB is the foundation for
cloud data, eliminating lock-in

36. 87
Resources to Get Started
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Free Tier today
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