11. 11
Reduce Risk for Mission-Critical
Deployments
Lower TCO
MongoDB Business Value
Leverage Data & Tech. to Maximize
Competitive Advantage
Faster Time to Value
40. 40
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
41. 41
Mobile Inbox
Mail app startup scales to 1M users in weeks
Problem Why MongoDB Results Problem Solution Results
Startup reimagines mobile inbox
Massive demand for its new mobile
email app
Needed ability to iterate quickly based
on early user feedback, stored variety
of data and metadata
Built app on MongoDB, leveraging
flexible data model to store variety of
inbox data and iterate quickly
Auto-sharding allowed the team to add
capacity in line with business growth
Secondary indexes allow for fast
access to data
Scaled business from 0 to over 1M
users within weeks, over 100M
messages/day
Delivered 3 major releases in 3
months
Acquired by Dropbox
42. 42
Case Study
Stores billions of posts in myriad formats with MongoDB
Problem Why MongoDB Results Problem Why MongoDB Results
1.5M posts per day,
different structures
Inflexible MySQL, lengthy delays for
making changes
Data piling up in production database
Poor performance
Flexible document-based model
Horizontal scalability built in
Easy to use
Interface in familiar language
Initial deployment held over 5B
documents and 10TB of data
Automated failover provides
high availability
Schema changes are
quick and easy
43. 43
Single View of Customer
Insurance leader generates coveted single view of
customers in 90 days – “The Wall”
Problem Why MongoDB Results Problem Solution Results
No single view of customer, leading to
poor customer experience and churn
145 years of policy data, 70+ systems,
24 800 numbers, 15+ front-end apps
that are not integrated
Spent 2 years, $25M trying build single
view with Oracle – failed
Built “The Wall,” pulling in disparate
data and serving single view to
customer service reps in real time
Flexible data model to aggregate
disparate data into single data store
Expressive query language and
secondary indexes to serve any field in
real time
Prototyped in 2 weeks
Deployed to production in 90 days
Decreased churn and improved ability
to upsell/cross-sell
44. 44
Real-Time Geospatial
Platform for Innovation
Using MongoDB to create a smarter and safer city
Problem Why MongoDB Results Problem Solution Results
Siloed data across city departments
made it difficult for the City of Chicago
to intelligently analyze situations
deliver services to its citizens
City needed a system that could not
only handle 7 million pieces of data /
day from different departments, but
also run analytics across it to deliver
insight
Used MongoDB’s flexible schema to build
the WindyGrid, a unified view of the city’s
operations that brings together disparate
datasets from 30 departments
Leveraged MongoDB’s rich analytics
features (aggregation framework,
geospatial indexes, etc) to create maps
that deliver real-time insight
Horizonal scalability with automatic
sharding across commodity servers
ensures the city can continue to cost
effectively deliver real-time results
A single view of the city’s operations on a
map of Chicago is now available to all
managers to help them better analyze and
respond to incidents in real-time
New predictive analytics system is
planned that will help prevent crimes
before they happen
450 sets of data have been published to
the public, sparking even further
innovation, e.g, an app that alerts citizens
when street sweepers are coming
46. 46
Agile Combustion Engine
• Agile methodologies do not guarantee project success
• It's a combined effort of
– Infrastructure
– Technical tools
– Organizational
– Business needs
• MongoDB helps on all these fronts
47. How a Database Can Make Your Organization Faster,
Better, Leaner
https://www.mongodb.com/collateral/how-database-can-
make-your-organization-faster-better-leaner