- MongoDB is a document database management system that is recognized as a leader by Gartner. It has over 520 employees, 2500+ customers, and offices globally. - MongoDB ranked 4th in database mindshare according to DB-Engines. It has seen 172% growth in the last 20 months. - Several companies such as a quantitative investment manager, an insurance company, a telecommunications company, and an ecommerce company migrated their systems to MongoDB and saw benefits like 100x faster data retrieval, 50% lower costs, and being able to build applications faster.

2. MongoDB Inc,
520+ employees 2500+ customers
Offices in NY, London & Palo Alto and
across EMEA, and APAC World Class Advisory

4. Gartner, Inc. recognized
MongoDB as a Leader
in the 2015 Magic Quadrant
for Operational Database
Management Systems.*
*Gartner, Inc., Magic Quadrant for Operational Database Management Systems by Donald Feinberg, Merv Adrian, Nick Heudecker, Adam M. Ronthal, and Terilyn Palanca, October 12, 2015.
*Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims
all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document.
The Gartner document is available upon request from MongoDB, Inc.

5. MongoDB ranked 4 in Database Mindshare
http://db-engines.com/en/ranking
RANK% DBMS% %%%%MODEL% SCORE% GROWTH%(20%MO)%
1.# Oracle# Rela+onal#DBMS# 1,442# 55%#
2.# MySQL# Rela+onal#DBMS# 1,294# 2%#
3.# Microso?#SQL#Server# Rela+onal#DBMS# 1,131# 510%#
4.% MongoDB% Document%Store% 277% 172%%
5.# PostgreSQL# Rela+onal#DBMS# 273# 40%#
6.# DB2# Rela+onal#DBMS# 201# 11%#
7.# Microso?#Access# Rela+onal#DBMS# 146# 526%#
8.# Cassandra# Wide#Column# 107# 87%#
9.# SQLite# Rela+onal#DBMS# 105# 19%#

6. Information Management Has Changed
UPFRONT SUBSCRIBE
Business
YEARS MONTHS
Applications
PC MOBILE
Customers
ADS SOCIAL
Engagement
SERVERS CLOUD
Infrastructure

7. Your Data Has Changed
• 90% of the world’s data was
created in the last two years
• 80% of enterprise data is
unstructured
• Unstructured data growing 2x
faster than structured

8. Big Data Driving Factors
“Of Gartner's "3Vs" of big data (volume, velocity, variety), the variety of data sources
is seen by our clients as both the greatest challenge and the greatest opportunity.”
2014
* From Big Data Executive Summary of 50+ execs from F100, gov orgs
What are the primary data issues driving you to consider Big Data?*
Data Variety (68%)
Data Volume (15%)
Other Data (17%)
Diverse, streaming or new data types
Greater than 100TB
Less than 100TB

9. MongoDB
Point of View

10. Relational
Expressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

11. The World Has Changed
Data Risk Time Cost

12. NoSQL
Scalability
& Performance
Always On,
Global Deployments
FlexibilityExpressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

13. Nexus Architecture
Scalability
& Performance
Always On,
Global Deployments
FlexibilityExpressive Query Language
& Secondary Indexes
Strong Consistency
Enterprise Management
& Integrations

14. Proof Points

15. Single Platform for Financial Data
Quantitative investment manager with over $11B in assets
under management invests heavily in new database
Problem% Why%MongoDB% Results%Problem Solution Results
AHL needed new technologies to be
more agile and gain competitive
advantages in the systematic trading
space
Proprietary systems in financial
services tech, as well as relational
databases, were too expensive and/or
rigid
Built single platform for all financial data
on MongoDB
Flexible data model and scalability
were core to ability to put all data in
single platform
Expressive query language, secondary
indexes and strong consistency were
core to ability to migrate core use cases
to new platform
100x faster to retrieve data
Tick Data: Quickly scaled to 250M ticks
per second, a 25x improvement in tick
throughput
Cut disk storage 60%, and realized
40% cost savings by using commodity
SSDs

16. Risk Mgt. for the
Connected Home
Delivering on customer protection mission with MongoDB
Problem% Why%MongoDB% Results%Problem Solution Results
Need to create innovative apps that
support corporate mission to protect
customers, improve brand perception,
and reduce churn
Poor view of customers’ behavior at
home, which can be used to provide
more compelling pricing and better
products
Unable to offer a new experience
around domestic connected objects
Build a scalable mobile app allowing
customers to integrate domestic
connected objects to detect and
prevent risk
Leverages flexible data model to
support specific APIs with third parties:
Philips Hue light bulbs, NEST smoke
detection, MyFox cameras, and more
in fast evolving market
Prototype built in 2 months; deployed
in production in 4 months
Able to prevent domestic risks and
assist customers in case of incidents.
Proactive alerting to customers
minimizes their risk, creates customer
loyalty
Great user experience improves
perception of AXA brand

17. Reference Data Management
Major app migrated to MongoDB, saving $40M over 5
years
Problem% Why%MongoDB% Results%Problem Solution Results
Globally distributed app reference data
management app did not meet SLAs
required in delivering data to traders,
resulting in SEC fines and damages to
the reputation of firm
Complex infrastructure with many
components (i.e., ETL, caching,
proprietary storage) was expensive and
difficult to maintain
Replatformed on MongoDB for a
simplified infrastructure
Native replication made it easy to
replicate to data centers on multiple
continents, bringing it closer to
stakeholders and reducing the effects
of geographic latency
$40M in savings over 5 years with
simplified infrastructure and the use of
commodity servers
Application in compliance with strict
SLAs

18. Content Management
Migrates from MySQL to MongoDB on AWS, saving £2M
and dramatically cutting project lead time
Problem% Why%MongoDB% Results%Problem Solution Results
Orange Digital web properties have
4.5M users on web and 2.3M users
on mobile, across www.orange.co.uk,
Orange World, and the Orange
Business site, and other digital assets.
MySQL reached scale ceiling
Metadata management too challenging
with relational model – targeting
handsets, users, types of data, video,
feeds, text and more
Replatformed on MongoDB and
migrated to AWS
Flexible data model makes it
substantially easier and more efficient
to manage variety of metadata
Sharding enables scalability and
unrivaled performance
Supports 115,000+ queries per second
Saved £2M+ over 3 yrs.
“Lead time for new implementations is
cut massively”
MongoDB is default choice for all new
projects
Eliminated 6B+ rows of attributes –
instead creates single document per
user / piece of content

19. Problem% Why%MongoDB% Results%Problem Solution Results
Proprietary solution with rigid data
model slowed rate of new service
introductions, impacting
competitiveness
Unable to scale as subscriber and
service portfolio expanded
High TCO incurred from proprietary
hardware and software
Built new customer data management
platform on MongoDB
Flexible data model enables dynamic
schema modification to support new
service introductions
Automatic sharding to scale database
as the business grows
MongoDB platform scales to serve
12M customers, with 50% reduced
cost per subscriber
Streamlined and simplified systems
allowing faster innovation and higher
agility
Migration to MongoDB completed in
just 6 months
Customer Data Mgt.
Telco leader unifies customer experience, driving 50% lower
cost and reduced churn

20. Personalization
Built personalization engine in 25% the time with 50% the
team
Problem% Why%MongoDB% Results%Problem Solution Results
Needed personalization server that acts
as the master storage for customer
data. Originally built on Oracle (over 14
months) but it performed below
expectations, did not scale, and cost
too much
New requirements made Oracle
unusable – 40% more data, must
reload entire data warehouse (22M
customers) daily in small window –
could not be met with Oracle
Implemented on MongoDB, using
flexible data model to easily bring in
data from disparate customer data
source systems
Expressive query language made it
possible to access customer records
using any field
Consulting and support significantly
reduced upfront development and
deployment costs
New version of personalization engine
was built on MongoDB in 25% the time
with 50% the team
Led to performance boosts of more
than a magnitude
Storage requirements decreased by
66%, lowering infrastructure costs