This presentation gives an overview of how Hortonworks and Red Hat have collaborated to provide the financial services industry with Big Data solutions.

1. Financial Services Industry Solution -
Risk Management
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Liquidity Risk

2. Liquidity Risk - The Definition
“Liquidity risk is financial risk due to uncertain liquidity.
An institution might lose liquidity if its credit rating falls, it experiences
sudden unexpected cash outflows, or some other event causes
counterparties to avoid trading with or lending to the institution.
A firm is also exposed to liquidity risk if markets on which it depends are
subject to loss of liquidity.”
(http://en.wikipedia.org/wiki/Liquidity_risk)
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3. Liquidity Risk - Business Challenge
● We need the ablility to run Liquidity Risk Algorithms on our Investment
Portfolios at many different intervals
○ Intra Day; End of Day; Weekly; Monthly; Quarterly; Annual
● In support of
○ maximizing investment opportunities
○ satisfying regulatory capital reserve requirements
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4. Liquidity Risk Calculations Demo Setup
We are actively ingesting real time market feeds into the architecture.
We will show:
● Intra Day Liquidity Risk Algorithms executing In Memory.
● Batch Liquidity Risk Algorithms executing in the Data Lake.
● Interactive SQL Queries showing Liquidity Risk Algorithm output.
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5. Overall Liquidity Risk Workflow
New Markets Data &
Portfolio Data &
Reference Data
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Red Hat
JBoss
Data Grid
HDP
Business
Users
(3.) In-Memory calculations take
place within Red Hat JBoss Data
Grid. Results are routed to HDP for
future Interactive and Batch job
flows.
(2.) Data Transformations take place in HDP. Data
flows back and forth between the Red Hat JBoss Data
Grid In Memory Compute Tier and HDP.
(1.) The Red Hat JBoss Data Grid
and Hortonworks Data Platform
(HDP) are primed with Real Time
and Batch Loads
(4.) Interactive SQL Queries and
Batch Job Execution take place
within HDP.
RDBMS
Optional Flow of Output
Data to RDBMS systems
(5.) Business Users leverage BI
Tools to Query Data Interactively in
HDP and / or an RDBMS

6. Liquidity Risk: High Level Topology
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Hortonworks Data Platform (HDP)
RDBMS
MPP
EDW
Governance
& Integration
Security
Operations
Data Access
Data Management
Red Hat JBoss Fuse
(ESB)
Red Hat JBoss BPMS
(CEP)
Red Hat JBoss Data Grid
(In-Memory Compute)
Red Hat JBoss BRMs
(Workflow)
Applications Analytics
Event Stream
and
Batch Loads
Red Hat JBoss
Data Virtualization
(ODBC, JDBC)
(Infinite Scale Storage and Compute)
Application Tier
Data Tiers
Data Ingestion

7. Liquidity Risk: Data Flow and Architecture
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Applications Analytics
Red Hat JBoss
Data Virtualization
Infinite Storage
Red Hat Storage or
HDFS
Input Feed
Interactive SQL
Hive
Output Feed
Red Hat JBoss BPMs
Write Once Read Many
Batch
Red Hat JBoss BRMS
(CEP)
Write Many Read Many
In Memory
Red Hat JBoss
Data Grid
RDBMS EDW MPP
Red Hat JBoss Fuse
(ESB) HDP

8. Page © Hortonworks Inc. 2014
DEMO

9. Appendix: FOR BUILD TEAM DISCUSSION
SE LAPTOP (Red Hat SE’s - 32GB RAM; Hortonworks SEs - 16 GB RAM)
PHASE 1
BUILDOUT
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Hortonworks Sandbox - HDP2.1
● HiveServer2
MySQL
RDBMS
Red Hat JBoss Data Virtualization
Red Hat JBoss Data Grid
SQuirrel
Libre Office
Note: Phase 2 (and for Fraud Solution) - Can add BI Partner and Red Hat JBoss BPMs, Red Hat JBoss Fuse