- MongoDB enables businesses to scale databases horizontally on commodity hardware or cloud infrastructure to handle terabytes or petabytes of data without downtime. It also allows easy adaptation by making flexible data modeling and adding new data types and sources simple. Additionally, MongoDB supports rich querying across diverse and changing data sets in real time to unlock insights from data. Case studies show how MongoDB has helped companies improve performance, innovate faster, and gain competitive advantages over relational databases.

1. MongoDB Inc. Proprietary and Confidential
3 Ways Modern Databases
Drive Revenue
Matt Asay, VP of Marketing

2. 2
The Last 40 Years Of Data:
Neat and Tidy

3. 3
Your Industry Has Changed
UPFRONT SUBSCRIBE
Business
YEARS MONTHS
Applications
PC MOBILE
Customers
ADS SOCIAL
Engagement
SERVERS CLOUD
Infrastructure

4. 4
Your Data Has Changed
• 90% of data created in the
last 2 years
• 80% of enterprise data is
unstructured
• Unstructured data growing
2x faster than structured
Sources: IBM, Gartner 2012

5. What Hasn’t Changed?

6. 6
How Do You Manage Big Data?
* From Big Data Executive Summary of 50+ execs from F100, gov orgs
Top Big Data Issues
“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.”
Forrester, 2014
Data Variety (68%)
Data Volume (15%)
Other Data (17%)
Diverse, streaming or new data types
Greater than 100TB
Less than 100TB

7. NoSQL ≠ NoSQL

8. 8
How a Modern Database Can
Change Your Business
Scale
Unlock
Adapt

9. Scale

10. 10
• Horizontal scale – on commodity
hardware or cloud – is mandatory
• Most apps require TBs of data, but you
want PBs of headroom
Your Database Must Not Throttle Success
Ambitious startup scale to 1M+ users in
weeks; 100s of millions of emails/month
Global media company scaled MongoDB to
4.5 PBs across public cloud infrastructure
Automated failover and ability to add nodes
means scale without downtime; “Blown
away by MongoDB’s performance”

11. 11
Powerful predictive analytics system that started
on Chief Data Officer’s laptop
Iterate…
Problem Results
• Diverse data from 30+
different government
agencies
• Limited budget – had to
prove the system to
justify budget
• Had to be able to
integrate geospatial
data with other highly
unstructured data
• Scales from single node
to many, many servers
• Easy-to-manage
dynamic data model
enables limitless growth
• Support for ad hoc
queries, geospatial
• Award-winning
government project
• Cost effective while
delivering exceptional
performance
• Easily extended to
incorporate new data
sources
Why MongoDB

12. Adapt

13. 13
Innovation Requires Iteration
New
Table
New
Table
New
Column
Name Pet Phone Email
New
Column
3 months later…

14. 14
RDBMS
From Complexity to Simplicity
MongoDB
{
_id : ObjectId("4c4ba5e5e8aabf3"),
employee_name: "Dunham, Justin",
department : "Marketing",
title : "Product Manager, Web",
report_up: "Neray, Graham",
pay_band: “C",
benefits : [
{ type : "Health",
plan : "PPO Plus" },
{ type : "Dental",
plan : "Standard" }
]
}

15. 15
• Break free of schema servitude: focus
on your app, not object-relational
mapping and rigid schema design
Your Database Must Make It Simple to
Add New Data Sources and Types
Struggled for years with RDBMS: schema
customization too difficult. MongoDB
“added flexibility and easy scalability”
Shaved years off projects to <4 months;
lowered TCO; no security compromise.
“Devs build apps w/out becoming DBAs”
Dramatically decreased drug development
time by making adding new data types
easy; integrates seamlessly with RDBMS

16. 16
Single view of customer data
(Virtually impossible with RDBMS)
Diverse Data…
Problem Why MongoDB Results
• 70+ disparate data
sources (maintframe,
RDBMS)
• RDBMS could not
support centralized data
mgt and federation of
information services
• Document model allows
easy integration of diverse
data sources
• Fast, easy scalability
• Full query language
• Delivers high scalability,
fast performance, and
easy maintenance, while
keeping support costs low
• Successful POC in 3
weeks; in production
within 90 days
• Single view of the
customer (improved
customer experience,
improved sales)
• 71% less expensive

17. Unlock

19. 19
• Storing data for fast access isn’t
enough. Questions matter most
• Database must support rich queries,
indexing, aggregation and search
across multi-structured, rapidly
changing data sets in real time
Your Database Must Enable Rich
Querying of Your Data
Transformed cumbersome data storage to
high-performance data analytics.
MongoDB-based Internet of Things platform
that takes advantage of ever-changing
sensor data and analytics against this data.
Runs unified data store serving hundreds of
diverse web properties on MongoDB

20. 20
50% increase in paid subscribers due to 95%
performance improvement over RDBMS
Multi-attribute Queries
Problem Why MongoDB Results
• RDBMS couldn’t handle
high-volume, bi-
directional searches
• Couldn’t persist a
billion-plus matches
• RDBMS was difficult to
manage in production
(schema changes were
painful; hard to scale)
• Ease of management:
auto-scaling, auto-
sharding, no downtime
• Complex queries across
250+ different attributes
• Exceptional performance
• Ability to dynamically
update schema without
complex schema redesign
• 95% performance
improvement: 3 billion
matches daily using 60
million complex queries
across 250+ attributes
• Big increase in
customer satisfaction,
paid subscribers
• Significantly less
expensive

21. “I have not failed. I've just found 10,000 ways that won't work.”
― Thomas A. Edison

22. 22
Optimize for (Developer) Iteration
1985 2013
Infrastructure Cost
Engineer Cost

23. 23
8,000,000+
MongoDB Downloads
Build on NoSQL’s Largest Ecosystem
1,000+
Customers Across All Industries; Hundreds of
Thousands of Users
600+
Technology and Services Partners
35,000+
MongoDB Management Service (MMS) Users
35,000+
MongoDB User Group Members
200,000+
Online Education Registrants

24. 24
MongoDB Can Help