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. This coupled with cost pressures from the business has led these institutions to seek alternatives. In this session learn how FS companies are using MongoDB to solve their problems. The use cases are specific to FS but the patterns of usage - agility, scale, global distribution - will be applicable across many industries.

3. 3
• Introduction
• We feel your pain
• Traditional Solutions not working
• What are your peers doing?
• How MongoDB has helped them
• Current Use Cases
• Future Opportunities

4. 4

5. 5

6. 6

7. 7
• Tick Data Capture & Analysis
• Reference Data Management
• Risk Analysis & Reporting
• Trade Repository
• Portfolio / Position Reporting
• Caching Tiers
• Mainframe offloading
• Know Your Client

10. 10
Use Case:
– Collect and aggregate risk data
– Calculate risk / exposures, potentially real time
Why MongoDB?
•Collect data from a single or multiple sources
•Support for polymorphic data formats.
•Documents used to create ‘pre-aggregated’ reports
•Aggregation Framework for reporting
•Map Reduce

11. 11
Reporting
Data Feeds

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• Capture real-time market data (multi-asset, top of
book, depth of book, even news)
• Load historical data
• Aggregate data into bars, daily, monthly intervals
• Enable queries & analysis on raw ticks or
aggregates
• Drive backtesting or automated signals

14. 14
Trades/metrics
Feed HandlerFeed Handler
Exchanges/Mark
ets/Brokers
Exchanges/Mark
ets/Brokers
Capturing
Application
Capturing
Application
Low Latency
Applications
Low Latency
Applications
Higher Latency
Trading
Applications
Higher Latency
Trading
Applications
Backtesting and
Analysis
Applications
Backtesting and
Analysis
Applications
Market Data
Cached Static &
Aggregated Data
News & social
networking
sources
News & social
networking
sources
Orders
Orders
Data Types
•Top of book
•Depth of book
•Multi-asset
•Derivatives (e.g. strips)
•News (text, video)
•Social Networking
Data Types
•Top of book
•Depth of book
•Multi-asset
•Derivatives (e.g. strips)
•News (text, video)
•Social Networking

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{
"_id" : ObjectId(“…"),
“ask" : 1.30028,
"bid" : 1.3002,
"ts" :ISODate("2012-02-
16T12:48:00Z")
}

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• How do you globally distribute reference data?
– Polymorphic data
• Price / Products / Securities Master
• Counterparty information - KYC
• Corporate Actions
• Golden / Single source truth
– Often changing in structure,
• e.g. new products
– Often High volume
• How is this typically solved today?

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• What do reference data solutions look like today?
• Storage
– Relational Database or Caching Technologies
• Replication
– ETL or Messaging
• Complex, Costly and Brittle
– Maintenance
• schema changes
• infrastructure
– Multiple technologies

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• What features in MongoDB are ideally suited for
Global replicated reference data systems?
1. Dynamic and flexible schema

20. 20
IssID IssuerName PVCurrency
117883 DWS Vietnam Fund USD
69461 Independence III Cdo Ltd USD
102862 Zamano Plc EUR
73277 Green Way BMD
65134 First European Growth Inc. CHF
SecID EventID Company_Meeting IssID
762288 407341 AGM 117883
81198 243459 SDCHG 69461
422999 410626 AGM 102862
422999 243440 SDCHG 102862
75128 20056 ISCHG 65134

21. 21
Relational MongoDB
{
"IssID" : 65134,
"IssuerName" : "First European
Growth Inc.",
"actions" : [
{
"Company_Meeting" :
"ISCHG",
"EventID" : 20056,
"SecID" : 75128
},
{
"Company_Meeting" :
"LSTAT",
"EventID" : 2716296,
"SecID" : 75128
}
]
}

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• What features in MongoDB are ideally suited for
Globally replicated reference data systems?
1. Dynamic and flexible schema
2. Built in replication and high availability

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• What features in MongoDB are ideally suited for
Globally replicated reference data systems?
1. Dynamic and flexible schema
2. Built in replication and high availability
3. Tag Aware Sharding (Geo)

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NA
EMEA
APAC

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

27. 27
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

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Distribute reference data globally in real-time for
fast local accessing and querying
Problem Why MongoDB Results
• Delays up to 20 hours in
distributing data via ETL
• Had to manage 20
distributed systems with
same data
• Incurring regulatory
penalties from missing
SLAs
• Stale data caused
operational issues
• Dynamic schema
management: update
immediately & in one
place
• Auto-replication: data
distributed in real-time
• 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
• Greater throughput means
charging more to internal
groups
• Network and disk speed is
the bottleneck, not
software and applications

30. 30
• Not Just Capital Markets
• Insurance / Consumer banking
• Inspiration from other Industries
– Telco
– Retail
• Opportunity to consolidate and utilise data in
interesting ways

33. 33
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