This document discusses accelerating digital transformation through a cloud data strategy using MongoDB.
It begins by outlining MongoDB's capabilities as a cloud data platform, including its use by over 3000 enterprises. The document then discusses how time to market has replaced cost as the primary driver for cloud adoption. It also outlines considerations for choosing a cloud data platform like deployment flexibility, reducing complexity, agility, resiliency, scalability, cost, and security.
The document then provides an overview of MongoDB's cloud offerings, including MongoDB Atlas on public clouds, MongoDB Ops Manager for private clouds, and MongoDB Stitch for backend services. It also discusses best practices for replatforming applications from relational databases to MongoDB in the cloud.
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Accelerating the Path to Digital with a Cloud Data Strategy
1. Accelerating the Path to Digital with a
Cloud Data Strategy
Accelerating the Path to Digital with a Cloud
Data Strategy
2. Agenda
Executing on a cloud data strategy to accelerate the
path to digital
Customer case study : TEG
Like vs Live : Emotional Data at scale
A route to the cloud: Best practices in replatforming
from relational databases to cloud-native MongoDB
3. Multi-model Built for mission critical
workloads
Adoption & Growth 3000+ Enterprise Customers 2500+ Atlas Customers
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. 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
8. Software is disrupting every industry
Source: Bureau of Economic Analysis
Manufacturing Retail Transportation Publishing,
Broadcast
Education,
Healthcare,
Social
Assistance
Finance,
Insurance,
Real Estate
Arts,
Entertainment,
Food
$1.6T
$1.1T
$1.5T
$6.2T
$5.3T
$2.4T
$1.2T
11. 11
Architecture is shifting
On premises / self-hosted
Monolithic
Proprietary
Fat Client / Web v1
Cloud
Microservices
Open source
Mobile
12. 12
...as is the Org Structure
Centralized IT
Hierarchical
Specialized
Process heavy
DevOps
Small, autonomous teams
Cross functional
Agile
(a la Amazon, Google, Netflix)
13. 13
API Access Layer
Operational Data
Customers
Products
Accounts
ML Models
Shared Physical Infrastructure
App1 App2 App3
1. Development Agility
– UI/API for self-service provisioning & scaling
2. Reusable / Flexible Data
– Each service’s data is physically isolated into
its own database instance
– Federated across services with appropriate
access controls
– Easily store and manipulate varied data
3. Governance and Control
– Logically managed as one service
– Audited and Compliant
Cloud Data Strategy
Standardized, On-Demand Database Service
Cloud Agnostic
Any Cloud, Any Where
14. 14
MongoDB
Purpose-built for large-scale cloud data
Enterprise-Grade Security
Highly Available/Global Deployments
Robust Monitoring,
Automation, and Backup
Distributed, Self-Healing
Transactional & Analytical
Workloads in a Single Platform
Multi-Platform, Multi-Cloud
18. 18
MongoDB Ops Manager
Management Platform for Private Cloud
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
20. 20
MongoDB Atlas: Public Cloud Flexibility
Cloud Portability — Maximize flexibility and avoid lock-in
21. 21
MongoDB Atlas: Database as a Service
Self-service, elastic,
and automated
Secure by default
Comprehensive
monitoring
Global and highly
available
Continuous
backups
22. 22
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
24. Use cases for MongoDB Stitch
Add a feature to an
existing application
• Include customer
reviews in an e-
commerce site
• Enrich profiles with
uploaded images
• Add comments to a
blog
Selectively expose
existing data to new
applications
• Display a games
leaderboard
• Provide an API for
internal apps to safely
access your
operational database
• Build admin-only page
with anonymized stats
Integrate with services
• Accept text
messages from Twilio
• Make a GitHub
commit trigger a
Slack
• Send email, text, or
Slack message
Be your complete
backend
• Use MongoDB
Stitch to orchestrate
multiple services
behind a unified,
secure backend API
25. 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 purpose-built for as
flexible, cloud-native platform
30. 30
Australian Open 2017
Melbourne
14 Days, 573 players,
728,763 fans attended
5297 racquets restrung
36,000+ hats sold
70,000+ New York
styled hot dogs
38. 38
Australian Open 2017
Melbourne
14 Days, 573 players,
728,763 fans attended
5297 racquets restrung
36,000+ hats sold
70,000+ New York
styled hot dogs
43. 43
Re-platforming to the Cloud
Mac McIntosh
Director of Pre-Sales & Services, APAC
mac.mcintosh@mongodb.com
44. 44
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
45. 45
AGILITY
• Flexible Data Model
• Native Drivers for Major Languages
• Single Technology Platform
• 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
46. 46
RESILIENCY
• Replica set: 3, 5 or 7 copy options in
Atlas
• 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
47. 47
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
48. 48
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, …
50. 50
• 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
51. 51
REDUCING COMPLEXITY THROUGHAMULTI-MODEL DB
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