1. Data Services for Service Oriented Architecture in Finance
D. Britton Johnston
Chief Technology Evangelist

2. Agenda Service Oriented Architecture
Ideal for high performance trading systems
SOA requires enterprise data architecture
Reliable, consistent and timely data
Trading system case studies
demonstrate benefits of well thought-out and executed data architecture for SOA

3. Case Example: Sell-Side Bank
Real-Time Trading Applications
Enterprise Service Bus
Application
Application
Application
Application
Application
Data Services
Integrated Data Access And Caching
Each application “re-invents” the data access layer:
Reduces developer productivity
Increases maintenance costs
Raises operating risks, system failures, downtime DB
40 global trading applications, $7B trades/day, over 5,000 txns/second

4. The Optimist’s View of SOA
Messaging Services
SOA
- SOAP
XML
UDDI/LDAP
Looser coupling of common tasks
Reuse at long last through shared services
Eliminates tyranny of silos
Everything just works™

5. Distribution Can Cause Bottlenecks
Business drivers: lower cost, higher flexibility
Technology enablers: grid computing, web services
App
Check_Avail
Check_Avail
Place_Order
Show_Status
Place_Order
Data
Data
Data
Show_Status
Shared Data DB DB DB DB DB DB
Apps share data cache, data silos can be out of sync
Each app requires separate data, all data must stay in sync

6. SOA Data Consistency Problem
Check_Avail
Place_Order
Data
Data
Nightly Sync
DB1
DB2
Item 3 = “in stock”
Item 3 = “out of stock”
Data silos can cause inconsistent results

7. The Pessimist's View of SOA
Looser coupling of commonly performed tasks…
But, tighter consistency for commonly used data
Reuse at long last through shared services…
But, lengthier development time for shared services
Eliminates tyranny of silos…
But, lose application boundaries
Everything just works™…
But, Nothing ever works as advertised™

8. Agenda Service Oriented Architecture
Ideal for high performance trading systems
SOA requires enterprise data architecture
Consistent and timely data
Trading system case studies
demonstrate benefits of well thought-out and executed data architecture for SOA

9. Requirements For Data Services
Functional Services
Data Caching Services: stage data with app for performance and scalability
App
App
App
Cache
Cache
Cache
Data Replication Services: position data for distributed computing, high availability
Data Services
Distributed Caching
Replication
O-R Mapping
Data Mapping Services: native language bindings for optimal performance DB DB DB

10. When To Worry: The 50/50 Rule
Requires model-driven O/R mapping
Requires data services layer
Model-intensive applications
Data-intensive applications
50+ classes
Requires intelligent caching
Object Model
Transaction-intensive applications
Basic applications
< 50 classes
< 50 TPS
50+ TPS
Request Rate
(Peak transactions/sec)

11. Real-Time Data Services “Stack”
Compute Grid
Distributed Execution
Flexibility – bindings
Performance –caching improves response time
Scalability –cache replication enables scaling
Availability–reliable sync improves app resilience
C++ App
Java App
C# App
Cache
Cache
Cache
Real-time Data Services
Distributed Caching
Data Integration
Virtualized Database
DB 1
DB 2
DB 3

12. The Iceberg Model For SOA
SOA Strengths
Loose task coupling
Reuse of shared tasks
Eliminate silos
Messaging Services
Functional Services
SOA Data Gotchas
Data consistency
Data bottlenecks
Data availability
Data Services
Legacy Environment

13. Agenda Service Oriented Architecture
Ideal for high performance trading systems
SOA requires enterprise data architecture
Consistent and timely data
Trading System Case Study
demonstrate benefits of well thought-out and executed data architecture for SOA

14. Case Study: Sell-Side Bank Business Requirements
Project Requirements
Front & middle office equity trading: >40 global apps
High transaction volumes: >5,000 TPS, millions per day
High availability: max downtime from failure <30 seconds
High scalability: support 5x volume at minimal cost
Reference data usage: all apps share common reference & order book data = huge potential for bottleneck
Deployment Architecture
Service Oriented Architecture: trading tasks exposed as shared functional services
Progress Real-time Data Services: Java binding, mapping, replication, caching
Grid Deployment: Unix Servers (>100 CPUs), Multi-site (US, Europe, Asia), Messaging Middleware, SQL Database

15. Case Study: Real-time Data Services Architecture
App examples
Trading desk, STP
Auto-exec engine
VWAP Pricing NY NJ NY
Vendor Feeds
Reuters
Bloomberg
Order Service
Reporting Service
Exchange Service
Counterparty Service
Counterparty Service
Counterparty Service
Validation
Workflow
Extract
Transform
Data cleanse
Change mgmt
Securities Service
Securities Service
Securities Service
Order Book Service
Real-time Data Services
Distributed Caching, Mapping, Synchronization
O/R Mapping
Caching
Replication
Partitioned databases
A-L
M-R
S-Z
Relational Databases

16. Case Study: Benefits Achieved
Scalability: grid data services infrastructure scaled to $7B/day in trades (mainframe maint savings > $4m/yr)
Availability: stateful failover between grid data services caches helped cut failover time from 5 min to 30 sec
Productivity: SOA delivered 50% productivity through service reuse, required up-front resource (~30% of team)
Grid Data Services: distributed caching required to “grid enable” stateful SOA services to run in compute grid

17. * Source: Progress customer case studies
ROI For SOA*
2x Developer productivity: reliable shared services should account for > 50% of new application functionality
3x maintenance productivity: systems deployed using SOA can be maintained with 75% fewer resources
2x “virtual data center” savings: distributed application deployment with centralized data storage (aka virtual data center) can achieve 40% capital cost savings, 30% annual operating cost savings vs traditional data centers
* Source: Progress customer case studies

18. SOA Data Architecture Roadmap
Consolidate SW infrastructure: eliminate silos, DBs (2+yrs)
3. Migrate functionality to SOA: plan to invest 30% of dev resources into shared services
1. Start with data virtualization: create “golden master” data
2. Add data services: provide consistent language bindings, distributed caching

19. Data Services for Service Oriented Architecture in Finance
D. Britton Johnston
Chief Technology Evangelist