This document discusses how MongoDB can help enable businesses to build and run modern applications. It begins with an overview of Tugdual Grall and his background. It then discusses how industries and data have changed, driving the need for a next generation database. The rest of the document provides an overview of MongoDB, including the company, technology, and community. Examples are given of how MongoDB has helped companies in the telecommunications industry achieve a single customer view, improve product catalogs and personalization, and build mobile and open data APIs.

1. Enabling Businesses to Build and Run Modern Applications
Tugdual Grall
Technical Evangelist
tug@mongodb.com
@tgrall

2. { “about” : “me” }
Tugdual “Tug” Grall
• MongoDB
– Technical Evangelist
• Couchbase
– Technical Evangelist
• eXo
– CTO
• Oracle
– Developer/Product Manager
– Mainly Java/SOA
• Developer in consulting firms
• Web
– @tgrall
– http://blog.grallandco.com
– tgrall
• NantesJUG co-founder
• Pet Project
– http://www.resultri.com
tug@mongodb.com
tugdual@gmail.com

3. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

4. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

5. Your Industry Has Changed
Upfront subscribe
Business
Years Months
Applications
PC Mobile
Customers
Ads social
Engagement
servers Cloud
Infrastructure

6. 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

7. You’re Not Alone
What are the primary data issues driving you to consider Big Data?*
*
From
Big
Data
Executive
Summary
of
50+
execs
from
F100,
gov
orgs
“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
Diverse, streaming or new data types
Greater than 100TB
Less than 100TB

8. Development – Methods are Changing
Requirements
Analysis
Design
Build
Test
Acceptance
Business
Input
Features

9. Development – Agile Development
Feature Backlog Working Product
Analysis
Design
Build
Test
2 - 4 Weeks Cycle

10. Software Has Changed
• High up-front costs
• High TCO
• Low up-front costs
• Low TCO

11. The Database is the
last technology in
the stack to be
modernized

12. Analytics & BI Integration

13. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

14. MongoDB, Inc.
400+ employees 1,000+ customers
Over $231 million in funding13 offices around the world

15. MongoDB Partners (600+)
Software & Services
Cloud & Channel Hardware

16. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

17. Data Types
Unstructured data
Semi-structured data
Polymorphic data
Agile Development
Iterative
Short development cycles
New workloads
Relational Database Challenges
Volume of Data
Petabytes of data
Trillions of records
Millions of queries/sec
New Architectures
Horizontal scaling
Commodity servers
Cloud computing

18. Operational Database Landscape
Scalability&Performance
Depth of Functionality
key/value stores
wide column
RDBMS
MongoDB

19. Changing Mindsets
Relational
Centralized
Document
Distributed

20. Removing Unneeded Complexity
{
name: ‘John Doe’,
id: ‘X2312-BC’,
cell: ‘+447557505611’
city: ‘London’,
location: [45.123,47.232],
plans: [
{ type : ‘mobile’
label: ‘30G+’,
price: 29.99,
… },
{ type : ‘internet’
label: ‘Cable’,
price: 39.99,
… }
}
}

21. Document Data Model
Relational MongoDB
{
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, … }
}
}

22. No SQLBut Still Flexible Querying
MongoDB
{
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, … }
}
}
Rich Queries
Find Paul’s cars
Find everybody in London with a car
built between 1970 and 1980
Geospatial
Find all of the car owners within 5km of
Trafalgar Sq.
Text Search
Find all the cars described as having
leather seats
Aggregation
Calculate the average value of Paul’s
car collection
Map Reduce
What is the ownership pattern of colors
by geography over time?
(is purple trending up in China?)

23. MongoDB - Scalability
• High Availability
• Auto Sharding
• Enterprise Monitoring
• Grid file storage

24. Morphia
MEAN
Stack
Java Python PerlRuby
Support for the most popular languages and frameworks
Drivers & Ecosystem

25. What We Sell
MongoDB Enterprise Advanced
The best way to run MongoDB in your data center
MongoDB Management Service (MMS)
The easiest way to run MongoDB in the cloud.
Production Support
In production and under control
Development Support
Let’s get you running.
Consulting
We solve problems.
Training
Get your teams up to speed.

26. ‹#›
DO YOU NEED: YES NO
Advanced security? ✓
Disaster Recovery? ✓
Monitoring for system performance and availability? ✓
Automated lifecycle management? ✓
Guaranteed response time? ✓
Platform certification ✓
Enterprise Decision Checklist

27. How MMS helps you
Scale
EasilyMeet
SLAs
Best
Practices,
Automated
Cut
Management
Overhead

28. What MMS can do
Provision
Upgrade
Scale
Continuous
Backup
Point-­‐in-­‐Time
Recovery
Performance
Alerts

29. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

30. THE LARGEST ECOSYSTEM
9,000,000+
MongoDB Downloads
250,000+
Online Education Registrants
35,000+
MongoDB User Group Members
40,000+
MongoDB Management Service (MMS) Users
750+
Technology and Services Partners
2,000+
Customers Across All Industries

31. Agenda
• The Need for a Next Generation Database
• MongoDB Overview
• The Company
• The Technology
• The Community
• MongoDB in Telco

32. Removing Impedance Mismatches
Object Relational
Mapping (ORM)
Extraction Transformation and
Loading (ETL)
Change
Management
Features vs
Complexity
Platform Agility

33. MongoDB Use Cases
Single View Internet of Things Mobile Real-Time Analytics
Catalog Personalization Content Management

34. Challenge: Achieve Cross Asset View
Batch
Batch
Batch
Issues
•Yesterday’s
data
•Details
lost
•Inflexible
schema
•Slow
performance
Batch
Impact
•What
happened
today?
•Worse
customer
satisfaction
•Missed
opportunities
•Lost
revenue
Batch
Batch
Reporting
Customers
Payments
Products
Data
Mart
Data
Mart
Data
Mart
Datawarehouse

35. .
.
.
.
Solution: Use New Database
Customers
Payments
Products
.
.
.
.
Operational
Data
Layer
Customers
Service
Operational
Reporting
Open
Data
API
Datawarehouse Strategic
Reporting
Benefits
• Real-­‐time
• Complete
details
• Agile
• Higher
customer
retention
• New
products
• …

36. Single View of Customer
Insurance leader generates coveted 360-degree view of
customers in 90 days – “The Wall”
Problem Why MongoDB Results
• No single view of
customer
• 145 yrs of policy data,
70+ systems, 15+ apps
• 2 years, $25M in failing
to aggregate in RDBMS
• Poor customer
experience
• Agility – prototype in 9
days;
• Dynamic schema & rich
querying – combine
disparate data into one
data store
• Hot tech to attract top
talent
• Production in 90 days with 70
feeders
• Unified customer view
available to all channels
• Increased call center
productivity
• Better customer experience,
reduced churn, more upsell
opps
• Dozens more projects on
same data platform

37. Single View of Customer
Adding Flexibility and Scalability to Bouygues Telecom
Problem Why MongoDB Results
• No single view of
customer
• Perfomance and
complexity
• 2 years delay
• Poor customer
experience
• Agility
• Scalability
• Dynamic schema & rich
querying – combine
disparate data into one
data store
• Easy data integration
• Developed in 6 months
• Unified customer view
available to all channels
• Increased call center
productivity
• New projects
• Devops

38. Product Catalog
Serves variety of content and user services on
multiple platforms to 7M web and mobile users
Problem Why MongoDB Results
• MySQL reached scale
ceiling – could not cope
with performance and
scalability demands
• Metadata management
too challenging with
relational model
• Hard to integrate
external data sources
• Unrivaled performance
• Simple scalability and
high availability
• Intuitive mapping
• Eliminated 6B+ rows of
attributes – instead
creates single document
per user / piece of content
• 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

39. Personnalisation Server
Accelerate Time To Market
Problem Why MongoDB Results
• Expensive Oracle Based
Solution
• 20 people, 16 months
• Performance issues
• 3 iterations
• Cannot take new
requirements
• Mature Technology
• Dynamic Schema
• Fault Tolerance
• Performance
• 4 Developers
• 4 months
• Add new features
• Faster
• Smaller
• Easier

40. Mobile / Open Data API
PIM Database
• Legacy Application
• Product Information
NoSQL
• REST API
• Product Data
• Additional Metadata

41. And many more…
Opening
new
possibles

42. Turning your Network into
Insights for resellers

43. Smartsteps

44. Ideas?

45. Conclusion
• World has changed
• Time To Market
• Cost Reduction
• New Possibles

46. Enabling Businesses to Build and Run Modern Applications
Tugdual Grall
Technical Evangelist
tug@mongodb.com
@tgrall