MongoDB Inc. Proprietary and Confidential

3 Ways Modern Databases Drive RevenueMatt Asay, VP of Marketing

2

The Last 40 Years Of Data:Neat and Tidy

3

Your Industry Has Changed

UPFRONT SUBSCRIBE

Business

YEARS MONTHSApplications

PC MOBILE

Customers

ADS SOCIALEngagement

SERVERS CLOUDInfrastructure

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

What Hasn’t Changed?

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

NoSQL ≠ NoSQL

8

How a Modern Database Can Change Your Business

Scale

Unlock

Adapt

Scale

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/monthGlobal media company scaled MongoDB to 4.5 PBs across public cloud infrastructureAutomated failover and ability to add nodes means scale without downtime; “Blown away by MongoDB’s performance”

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

Adapt

13

Innovation Requires Iteration

New Table

New Table

New Column

Name Pet Phone Email

New Column

3 months later…

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

• 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

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

Unlock

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

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

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

22

Optimize for (Developer) Iteration

1985 2013

Infrastructure Cost

Engineer Cost

23

8,000,000+ MongoDB Downloads

Build on NoSQL’s Largest Ecosystem

1,000+ Customers Across All Industries; Hundreds ofThousands 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

MongoDB Can Help