Huge upheaval in the finance industry has led to a major strain on existing IT infrastructure and systems. New finance industry regulation has meant increased volume, velocity and variability of data, so-called Big Data. This coupled with cost pressures from the business has led these institutions to seek alternatives. Top tier institutions like MetLife have turned to MongoDB because of the enormous business value it enables. In this session, learn where and how you should use MongoDB to get the maximum value including specific case studies such as saving $40M in one project. The use cases are specific to financial services but the patterns of usage - agility, scale, global distribution - will be applicable across many industries.

1. Driving Business Value in FS
with MongoDB
Matt Kalan, FS Solutions Architect
Email: Matt.kalan@10gen.com
Twitter: @matthewkalan

2. 2
• FS requirements today
• Traditional approaches
• MongoDB approach
• Rapid adoption
• Technical overview
• Best fit usage patterns
• Case studies
Agenda

3. 3
Trends in FS Driving Change
Mobile &
Gamification (retail)
New
Opportunities
Regulations
Enhanced Risk
Management
CCP
Central
Clearing
Cost
pressures

4. 4
Requirements
• Aggregate disparate
data
• Change apps quickly
• Analyze data faster
• Store more data
• Increase productivity
• Reduce TCO
drastically
Putting Urgency on These IT
Requirements Like Never Before

5. 5
Unfortunately, RDBMSs Not Built for
These Requirements
Data Types & OOP
• Unstructured data
• Semi-structured
data
• Polymorphic data
Volume of Data
• Petabytes of data
• Trillions of records
• Tens of millions of
queries per second
Agile Development
• Iterative
• Short development
cycles
• New workloads
New Architectures
• Horizontal scaling
• Commodity
servers
• Cloud computing

6. 6
• Customfield1…100 or separate tables
• Caching & ORMs
• Expensive hardware and storage
• Schema migration
• One schema across apps
• Application-specific partitioning
• Use files instead of databases
• Schema change takes 6 months
As a Result, Shoehorn Requirements
Slow time-to-market
Agility lost
High cost
Business frustrated

7. 7
Now There Is Help
2010
RDBMS
Key-Value/
Column Store
OLAP/BI
Hadoop
2000
RDBMS
OLAP/BI
1990
RDBMS
Operational
Data
Datawarehouse
Document DB
NoSQL

8. 8
Database Landscape
• No Automatic Joins
• Document Transactions
• Fast, Scalable Read/Writes

9. 9
Relational: All Data is Column/Row
Customer ID First Name Last Name City
0 John Doe New York
1 Mark Smith San Francisco
2 Jay Black Newark
3 Meagan White London
4 Edward Daniels Boston
Account Number Branch ID Account Type Customer ID
10 100 Checking 0
11 101 Savings 0
12 101 IRA 0
13 200 Checking 1
14 200 Savings 1
15 201 IRA 2

10. 10
Have to Manage Change in 3 Places
Relational
Database
Object Relational
Mapping
Application
Code XML Config DB Schema

11. 11
Instead Match the Data in your
Application
Relational MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
accounts : [ {
account_number : 13,
branch_ID : 200,
account_type : "Checking"
},
{ account_number : 14,
branch_ID : 200,
account_type : ”IRA”,
beneficiaries: […]
} ]
}

12. 12
Instead Put Data Model in One Place
Application
Code
Relational
Database
Object Relational
Mapping
XML Config DB Schema
Application
Code
Rich
Queries
Geospatial
Text Search
Map Reduce
Aggregatio
n

13. 13
No SQL But Still Flexible Querying
MongoDB
{ customer_id : 1,
first_name : "Mark",
last_name : "Smith",
city : "San Francisco",
accounts : [ {
account_number : 13,
branch_ID : 200,
account_type : "Checking"
},
{ account_number : 14,
branch_ID : 200,
account_type : ”IRA”,
beneficiaries: […]
} ]
}
Rich Queries
• Find all Mark’s accounts
• Find everybody who opened an account
last month
Geospatial
• Find all customers that live within 10
miles of NYC
Text Search
• Find all tweets that mention the
company within the last 2 days
Aggregation
• What’s the average value of Mark’s
accounts
Map Reduce
• How many customers that have a
checking account also have an IRA

14. 14
Operational Database Use Cases
RDBMSs
Key/Value or
Column Stores
MongoDB

15. 15
DB-Engines.com Ranks DB Popularity

16. 16
• MetLife Leapfrogs Insurance Industry with MongoDB-Powered
Big Data Application
– “innovative customer service application…in 90 days…from 70+ existing systems”
• 10gen Establishes Financial Services Advisory Group
– “ten leading global institutions…including Barclays, Goldman Sachs and MetLife.”
• IBM and 10gen Collaborate to Bring Mobile to the Enterprise
– “IBM will standardize on BSON, MongoDB wire protocol and query language”
• Informatica and 10gen Partner to Expand Data Integration for
MongoDB
– “use PowerCenter Big Data Edition to access…data stored in the market's leading
NoSQL database”
Customers and Software
Heavyweights Support MongoDB

17. 17
10gen Partners (150+) & Integration
Software & Services
Cloud & Channel Hardware

18. MongoDB Solution

19. 19
MongoDB Business Benefits
Increased Developer Productivity Better Customer Experience
Faster Time to Market Lower TCO

20. 20
MongoDB Technical Benefits
Horizontally Scalable
-Sharding
Agile &
Flexible
High
Performance
-Indexes
-RAM
Application
Highly
Available
-Replica Sets
{ name: “Mark Smith”,
date: “2013-08-01”),
address: “10 3rd St.”,
phone: [
{ home: 1234567890},
{ mobile: 1234568138} ]
}
db.cust.insert({…})
db.cust.find({
name:”Mark Smith”})

21. Best Fit for MongoDB

22. 22
• New Application DB - normal real-time/OLTP application
database for new application
• Migrating Existing DB - migrated from RDBMS where
scale, agility, and/or cost is an issue
• Data Hub Above Core DBs/Apps – Layer above multiple
systems for real-time/request-response access
• Data Hub For Single Operational View - Single data
store from many disparate apps
• Data PaaS - Unlimited scalable data services as PaaS
Most Common Usage Patterns of
MongoDB

23. 23
New Application Operational DB
MongoDB Cluster
Application
…
MongoDB
Driver/API
Datawarehouse
Environment

24. 24
Migrating Operational DB
Application
Pain from performance, agility, or cost?
=> Best candidate for MongoDB
MongoDB Driver/API
Data
Access
Object/
Layer
ODBC

25. 25
Application
Server
Fast Access Layer Above Core Apps
Application 1
MongoDB
Cluster
Application 2
Services
Layer
Application N
…
…
Mainframe
Core
Systems
RDMS
Core/legacy
Systems
ETL or
Pub/sub
REST/WS/API

26. 26
Single View Across Disparate Systems
Source
database 1
Source
database 2
…
Source
Database N
• ETL
• File export
• Custom app
• Pub/sub
Document
• per record in
source system
Application 1
Application 2
Application M
OLTP/real-time
access
Queue to Update
Source Systems
…

27. 27
Database PaaS
Application 1
MongoDB Cloud
Application 2
Application N
…
…

28. 28
Common FS Use Cases
Capital Markets
1. Reference Data
Management
2. Risk Analysis &
Reporting
3. Private DBaaS
4. Buy-Side Portal
5. Regulatory Reporting
6. Trade Repository
7. Tick Data Capture &
Analysis
8. Order Capture
Banking
1. Single View of
Customer
2. Online Banking
3. Reference Data
Management
4. Risk Analysis &
Reporting
5. Product Catalog
6. Cybersecurity
Threat Analysis
Insurance
1. Single View of
the Customer
2. Online Quoting
3. Customer Portal
4. Risk Analysis &
Reporting
5. Reference Data
Distribution
6. Policy Definition
Catalog

29. 29
Common FS Use Cases
Capital Markets
1. Reference Data
Management
2. Risk Analysis &
Reporting
3. Private DBaaS
4. Buy-Side Portal
5. Regulatory Reporting
6. Trade Repository
7. Tick Data Capture &
Analysis
8. Order Capture
Banking
1. Single View of
Customer
2. Online Banking
3. Reference Data
Management
4. Risk Analysis &
Reporting
5. Product Catalog
6. Cybersecurity
Threat Analysis
Insurance
1. Single View of
the Customer
2. Online Quoting
3. Customer Portal
4. Risk Analysis &
Reporting
5. Reference Data
Distribution
6. Policy Definition
Catalog

30. 30
Distribute reference data globally in real-time for
fast local accessing and querying
Data Hub - Fast Access Case Study:
Global investment bank
Problem Why MongoDB Results
• Delays up to 36 hours in
distributing data by batch
• Charged multiple times
globally for same data
• Incurring regulatory
penalties from missing
SLAs
• Had to manage 20
distributed systems with
same data
• Dynamic schema: easy to
load initially & over time
• Auto-replication: data
distributed in real-time,
read locally
• Both cache and database:
cache always up-to-date
• Simple data modeling &
analysis: easy changes
and understanding
• Will save about
$40,000,000 in costs and
penalties over 5 years
• Only charged once for data
• Data in sync globally and
read locally
• Capacity to move to one
global shared data service

31. 31
Previous Reference Data Management
Architecture
Feeds & Batch data
• Pricing
• Accounts
• Securities Master
• Corporate actions
Source
Master Data
(RDBMS)
Batch
Batch Batch
Batch
Batch
Batch
Batch
Destination
Data
(RDBMS)
Each represents
• People $
• Hardware $
• License $
• Reg penalty $
• & other downstream
problems

32. 32
Solution with MongoDB
Feeds & Batch data
• Pricing
• Accounts
• Securities Master
• Corporate actions
Real-time
Real-time Real-time
Real-time
Real-time
Real-time
Real-time
Each represents
• No people $
• Less hardware $
• Less license $
• No penalty $
• & many less
problems
MongoDB
Secondaries
MongoDB
Primary

33. 33
Global 360 degree view of customers’ policy portfolio
and interactions
Single View of Customer Case Study:
Tier 1 Global Insurance Provider
Problem Why MongoDB Results
• 70 systems and 20
screens to view
customer policies
• Many CSR calls taken
just to reroute customer
• Poor customer
experience
• Source systems are
hard to change
• Dynamic schema: can
combine 70 systems
easily
• Performance: can handle
all data in one DB
• Replication: local reads
and high availability
• Sharding: can add data
easily by scaling out
• Delivered in 3 months
with $4M – previous
attempts failed with $25M
• Unified customer view
available to all channels
• Shorter and less calls re-
routed
• Increased customer
satisfaction

34. 34
Single View of Customer Case Study:
Tier 1 Global Insurance Provider
Source
database 1
Source
database 2
…
Source
Database 70
Custom app
exports JSON
Document
• per product
• per customer
CSR Application
Customer
Application
Agent/RM
Application
OLTP/real-time
access
Future phases
Queue to Update
Source Systems

35. 35
More timely and accurate market risk analysis
Migrating Application DB Case Study:
Global FS Provider
Problem Why MongoDB Results
• Merger brought many
more users onto system
• Fed requiring longer
time window
• Need for versioning for
data lineage & auditing
• Could not scale existing
RDBMS
• Performance: can handle
more users and more
data all at once
• Dynamic schema: can
store disparate data and
make changes easily
• Replication: local reads
and high availability
• Sharding: can add data
easily by scaling out
• Risk analysis performed
every 15 minutes instead
of daily
• Have full audit trail of state
of the world at any time
• Can make application
changes much faster
• Trading desks can hedge
more effectively and use
more capital

36. 36
Aggregating Risk Reporting
Reporting
Trades in
real-time

37. 37
Online Banking/Trading Portal
Use case requirements:
• Store portfolios, accounts, positions/balances, orders, market values, etc.
• Ad hoc querying by account, security, date, trader, thresholds, etc.
• Fast response times and iteration keep customer satisfaction high
• Relationship manager wants real-time reporting and alerting on customer
activity
Why MongoDB?
• Low latency & caching => fast response times for all data available
• Dynamic schema => Can handle any portfolio structure, assets, or accounts
• High scalability => Reporting requirements on often large customer data
sets
• Aggregation Framework => calculate metrics, aggregations, and analysis

38. 38
70%+ Lower TCO + New Capabilities
Commercial RDBMS
Compute – Scale-Up Servers
Storage – SAN
Dev. and Admin
Compute – Commodity HW
Storage – Local Storage
Dev. and Admin
$1,680K
$517K

39. 39
• FS today requires agility, productivity, and low TCO
• RDBMS not supporting requirements well
• MongoDB addresses all these requirements as a general
purpose operational DB
• MongoDB has hit critical mass in adoption
• The best fit usage patterns are everywhere
• Many case studies demonstrating value in FS
• Let us know if we can help you get started
Summary

40. 40
Training
Online and In-Person for Developers and Administrators
MongoDB Monitoring Service
Free, Cloud-Based Service for Monitoring and Alerts
MongoDB Backup Service
Cloud-Based Service for Backing Up and Restoring MongoDB
10gen Products and Services
Subscriptions
MongoDB Enterprise, Monitoring, Support, Commercial License
Consulting
Expert Resources for All Phases of MongoDB Implementations

41. 41
Resource Location
MongoDB Downloads 10gen.com/download
Free Online Training education.10gen.com
Webinars and Events 10gen.com/events
White Papers 10gen.com/white-papers
Case Studies 10gen.com/customers
Presentations 10gen.com/presentations
Documentation docs.mongodb.org
Additional Info info@10gen.com
For More Information
Resource Location