Big Data Analytics is transforming how banks and financial institutions unlock insights, make more meaningful decisions, and manage risk. Join this webinar to see how you can gain a clear understanding of the customer journey by leveraging Platfora to interactively analyze the mass of raw data that is stored in your Hortonworks Data Platform. Our experts will highlight use cases, including customer analytics and security analytics. Speakers: Mark Lochbihler, Partner Solutions Engineer at Hortonworks, and Bob Welshmer, Technical Director at Platfora

1. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
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2. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Customer Analytics and Risk Management
in Financial Services
We do Hadoop.

3. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Your speakers…
Mark Lochbihler, Partner Solutions Engineer
Hortonworks
@MarkLochbihler
Bob Welshmer, Technical Director,
Strategic Accounts
Platfora
@BDubya22

4. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Our Mission:
Power your Modern Data Architecture
with HDP and Enterprise Apache Hadoop
Customer Momentum
• 300+ customers in seven quarters, growing at 75+/quarter
• Two thirds of customers come from F1000
Hortonworks and Hadoop at Scale
• HDP in production on largest clusters on planet
• Multiple +1000 node clusters, including 35,000 nodes at
Yahoo!, 800 nodes at Spotify
• Founded in 2011
• Original 24 architects, developers,
operators of Hadoop from Yahoo!
• We are leaders in Hadoop community
• 500+ employees

5. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
The Forrester Wave™
Big Data Hadoop Solutions
Q1 2014
“Hortonworks loves and lives
open source innovation”
World Class Support and Services.
Hortonworks' Customer Support received a
maximum score and was significantly higher
than both Cloudera and MapR
A Leader in Hadoop

6. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Hortonworks Approach
Innovate the Core1
Architect and build
innovation at the core of
Hadoop
• YARN: Data Operating System
• HDFS as the storage layer
• Key processing engines
Extend Hadoop as an
Enterprise Data Platform
2 Enable the Ecosystem3
Extend Hadoop with enterprise
capabilities for governance,
security & operations
Apply enterprise software rigor
to the open source development
process
Enable the leaders in the data
center to easily adopt & extend
their platforms
• Establish Hadoop as standard
component of a modern data
architecture
• Joint engineering
YARN
:
Data
Opera.ng
System
Script
Pig
SQL
Hive/Tez,
HCatalog
NoSQL
HBase
Accumulo
Stream
Storm
Batch
Map
Reduce
HDFS
(Hadoop
Distributed
File
System)
HDP 2.2
Governance
&Integration
Security
Operations
Data Access
Data Management
YARN
Memory
Spark

7. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Enabling a Modern Data Architecture
with HDP and Apache Hadoop
Hortonworks. We do Hadoop.

8. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
APPLICATIONSDATASYSTEM
Business
Analytics
Custom
Applications
Packaged
Applications
Traditional systems under pressure
• Silos of Data
• Costly to Scale
• Constrained Schemas
Clickstream
Geolocation
Sentiment, Web Data
Sensor. Machine Data
Unstructured docs, emails
Server logs
SOURCES
Existing Sources
(CRM, ERP,…)
RDBMS EDW MPP
New Data Types
…and difficult to
manage new data

9. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
HDP2 and YARN enable the Modern Data Architecture
Hortonworks architected and
led development of YARN
Common data set, multiple applications
• Optionally land all data in a single cluster
• Batch, interactive & real-time use cases
• Support multi-tenant access, processing
& segmentation of data
YARN: Architectural center of Hadoop
• Consistent security, governance & operations
• Ecosystem applications certified
by Hortonworks to run natively in Hadoop
SOURCES
EXISTING
Systems
Clickstream
Web
&Social
Geoloca.on
Sensor
&
Machine
Server
Logs
Unstructured
APPLICATIONSDATASYSTEM
Business
Analytics
Custom
Applications
Packaged
Applications
RDBMS EDW MPP YARN: Data Operating System
1 ° ° ° ° ° ° ° ° °
° ° ° ° ° ° ° ° ° N
HDFS
(Hadoop Distributed File System)
Interactive Real-TimeBatch

10. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
1. Unlock New Applications from New Types of Data
INDUSTRY USE CASE
Sentiment
& Web
Clickstream
& Behavior
Machine
& Sensor
Geographic Server Logs
Structured &
Unstructured
Financial Services
New Account Risk Screens ✔ ✔
Trading Risk ✔
Insurance Underwriting ✔ ✔ ✔
Telecom
Call Detail Records (CDR) ✔ ✔
Infrastructure Investment ✔ ✔
Real-time Bandwidth Allocation ✔ ✔ ✔
Retail
360° View of the Customer ✔ ✔ ✔
Localized, Personalized Promotions ✔
Website Optimization ✔
Manufacturing
Supply Chain and Logistics ✔
Assembly Line Quality Assurance ✔
Crowd-sourced Quality Assurance ✔
Healthcare
Use Genomic Data in Medial Trials ✔ ✔ ✔
Monitor Patient Vitals in Real-Time ✔ ✔
Pharmaceuticals
Recruit and Retain Patients for Drug Trials ✔ ✔
Improve Prescription Adherence ✔ ✔ ✔ ✔
Oil & Gas
Unify Exploration & Production Data ✔ ✔ ✔ ✔
Monitor Rig Safety in Real-Time ✔ ✔ ✔
Government
ETL Offload/Federal Budgetary Pressures ✔ ✔
Sentiment Analysis for Government Programs ✔

11. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
..to shift from reactive to proactive interactions
HDP and Hadoop allow
organizations to shift
interactions from…
Reactive
Post Transaction
Proactive
Pre Decision
…to Real-time PersonalizationFrom static branding
…to repair before breakFrom break then fix
…to Designer MedicineFrom mass treatment
…to Automated AlgorithmsFrom Educated Investing
…to 1x1 TargetingFrom mass branding
A shift in Advertising
A shift in Financial Services
A shift in Healthcare
A shift in Retail
A shift in Telco

12. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
2. Or to realize a dramatic cost savings…
✚
EDW Optimization
OPERATIONS
50%
ANALYTICS
20%
ETL PROCESS
30%
OPERATIONS
50% ANALYTICS
50%
Current Reality
EDW at capacity: some usage
from low value workloads
Older data archived, unavailable
for ongoing exploration
Source data often discarded
Augment w/ Hadoop
Free up EDW resources from
low value tasks
Keep 100% of source data and historical
data for ongoing exploration
Mine data for value after loading it
because of schema-on-read
Hadoop
Parse, Cleanse
Apply Structure, Transform

13. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
2. Or to realize a dramatic cost savings…
✚
EDW Optimization
OPERATIONS
50%
ANALYTICS
20%
ETL PROCESS
30%
OPERATIONS
50% ANALYTICS
50%
Current Reality
EDW at capacity: some usage
from low value workloads
Older data archived, unavailable
for ongoing exploration
Source data often discarded
Augment w/ Hadoop
Free up EDW resources from
low value tasks
Keep 100% of source data and historical
data for ongoing exploration
Mine data for value after loading it
because of schema-on-read
MPP
SAN
Engineered System
NAS
HADOOP
Cloud Storage
$0 $20,000 $40,000 $60,000 $80,000 $180,000
Fully-loaded Cost Per Raw TB of Data (Min–Max Cost)
Commodity Compute & Storage
Hadoop Enables Scalable Compute &
Storage at a Compelling Cost Structure
Hadoop
Parse, Cleanse
Apply Structure, Transform
Storage Costs/Compute Costs
from $19/GB to $0.23/GB

14. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
3. Data Lake: An architectural shift
SCALE
SCOPE
Unlocking the Data Lake
RDBMS
MPP
EDW
Data Lake
Enabled by YARN
• Single data repository,
shared infrastructure
• Multiple biz apps
accessing all the data
• Enable a shift from
reactive to proactive
interactions
• Gain new insight across
the entire enterprise
New Analytic Apps
or IT Optimization
HDP 2.2
Governance
&Integration
Security
Operations
Data Access
Data Management
YARN

15. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
© Hortonworks Inc. 2014 - Confidential
Banking Data Lake for 100s of Use Cases
Page 15
Problem
Architecture unsuited to capitalize on server log data
• Huge investments company generates valuable data assets
• Current EDW solutions are appropriate for some data workloads but too expensive
for others
• Financial log data is difficult to aggregate & analyze at scale
• Short retention hampers price history & performance analysis
• Limited visibility into cost of acquiring customers
Solution
Multi-tenant Hadoop cluster to merge data across groups
• Server log data merged with structured data to uncover trends across traders
• ETL offload saves money for Hadoop-appropriate workloads
• Longer data retention enables price history analysis
• Joining data sets for insight into customer acquisition costs
• Accumulo enforces read permissions on individual data cells
Investment
Services
Global investments company
> $1.5 trillion assets under
management
> $14B billion in revenue
~ 50K employees

16. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
HDP delivers a comprehensive data management platform
Hortonworks Data Platform 2.2
YARN: Data Operating System
(Cluster Resource Management)
1 ° ° ° ° ° ° °
° ° ° ° ° ° ° °
Script
Pig
SQL
Hive
Tez
Tez
Java
Scala
Cascading
Tez
° °
° °
° ° ° ° °
° ° ° ° °
Others
ISV
Engines
HDFS
(Hadoop Distributed File System)
Stream
Storm
Search
Solr
NoSQL
HBase
Accumulo
Slider
Slider
SECURITYGOVERNANCE OPERATIONSBATCH, INTERACTIVE & REAL-TIME DATA ACCESS
In-Memory
Spark
Provision,
Manage &
Monitor
Ambari
Zookeeper
Scheduling
Oozie
Data Workflow,
Lifecycle &
Governance
Falcon
Sqoop
Flume
Kafka
NFS
WebHDFS
Authentication
Authorization
Accounting
Data Protection
Storage: HDFS
Resources: YARN
Access: Hive, …
Pipeline: Falcon
Cluster: Knox
Cluster: Ranger
Deployment ChoiceLinux Windows On-Premises Cloud
YARN
is the architectural
center of HDP
Enables batch, interactive
and real-time workloads
Provides comprehensive
enterprise capabilities
The widest range of
deployment options
Delivered Completely in the OPEN

17. Introducing Platfora
LEAD THE INDUSTRY TRANSITION FROM
BUSINESS INTELLIGENCE TO BIG DATA
ANALYTICS.
#1 Big Data Analytics
platform native on
Hadoop
MISSION
End-to-end platform built for
Multi-Structured Data
Self-service, iterative,
interactive, and fast

18. WORLD CLASS CUSTOMERS
Proven Leader in Big Data Analytics for Hadoop
PROVEN COMPANY TO WATCH
Ones to Watch in Big
Data
April 2014 10 Hot
Hadoop
Startups to Watch
CRN 10 Coolest Big
Data Products of
2013
RAISED $65M BY LEADING INVESTORS
• Launched 9 product versions with
feature innovations
• Grew customers by 4x and employees
by 2x
MOMENTUM OVER THE
PAST 12 MONTHS

19. The Way We Access and Interact with Data has
Changed
Data Warehousing: 1980s Technology that Still Exists Today
1. 1980s: DATA WAREHOUSING 2. EARLY 2000s: HADOOP 3. PLATFORA’S DISRUPTION
3-6 Months
ETL
Personnel Needed
ETL Programmer
RAW DATA
Customer Interactions
Machine Data
Transactions
90% of copy trashed
Data
Warehouse
Expensive $$$
Personnel Needed
Data Warehouse
Architect & Admin
BI Tool
Personnel Needed
BI Architect
& Admin
Business
Analyst
Outcome
Business Analyst gets
the data after 3-6
months with little control.
Rinse and repeat.

20. The Way We Access and Interact with Data has
Changed
Hadoop: Early 2000s New Data Storage Technology Arrives
RAW
DATA
1. 1980s: DATA WAREHOUSING 2. EARLY 2000s: HADOOP 3. PLATFORA’S DISRUPTION
3-6 Months + +
H A D O O P
MapReduce
Hive
Pig
Personnel Needed
Hadoop Expert
Data Scientist
ETL
SQL
on
Hadoop
Or Data Warehouse
Personnel Needed
Data Warehouse
Architect & Admin
BI Tool
Personnel Needed
BI Architect
& Admin
Business
Analyst
Outcome
Business Analyst gets
the data after 3-6
months with little control.
Rinse and repeat.

21. The Fastest Way to go from Raw Data to Analytics
Platfora is Leading the Transition from Business Intelligence to Big Data
Analytics
RAW
DATA
1. 1980s: DATA WAREHOUSING 2. EARLY 2000s: HADOOP 3. PLATFORA’S DISRUPTION
H A D O O P
Minutes
Business Analyst iterates & repeats
No Additional
Personnel Needed
Easily accessible by
Data Admins & Data
Scientists
Business
Analyst
Outcome
Business Analyst gets
the data in minutes,
collaborates with team,
and ask new questions
quickly.

22. The Only Platform that Offers an End-to-End
Architecture
Business
Analyst
Interactive Big Data
Analytics
Data Preparation
HDFS & Other Data
Sources
RAW DATA &
DATA
CONNECTORS
Transactions Customer
Interactions
Machine
Security
DataCatalog
API/Extensions

23. A Truly Scalable Platform
MapReduce/ Spark
Lens
Raw data at PB+ scale Accessible data at TB+ scale
HADOOP PLATFORA
Platfora natively leverages the deep processing, scalability, and limitless data
storage of Hadoop and combines it with a scale-out in-memory data
processing engine to make access extremely fast across infinite nodes.

24. Unlocking Big Data Analytics Solutions
• Data Exfiltration Monitoring
• Advanced Threat Analysis
• Patch and Version Coverage
Security Analytics
• Omni-channel Conversion Analysis
• Audience Segmentation
• Behavior Analysis
Customer Analytics
• Consumer Devices and Monitoring
• Utility Usage Monitoring
• Product Telemetry
Internet of Things
• No limit to new use
cases / verticals
• Highly leveraged
platform services
• Partners can
add custom IP
Open Solution
Platform
Customer
Analytics
Internet
of
Things
Security
Analytics

25. Detecting Advanced Persistent Cybersecurity
Attacks
“Platfora was built from the ground up for Hadoop.
Other players have been around longer and they are
all trying to shoehorn themselves into the Hadoop
infrastructure. And being architected for Hadoop is
very different from creating a Hadoop connector.
That’s a big differentiator for Platfora.”
Chief Information Security Officer
Multiple
Large
Financial
Organization
s
The Business Challenge
• MicroStrategy and other traditional BI tools
could not handle the volume of data that this
financial org was ingesting
• Needed to be able to respond dynamically
and instantly to any risk of malicious in-
network activity
The Solution
• Identified malicious in-network activity that had
stayed under the radar of traditional security
solutions
• Combined internal, netflow, firewall, IDS,
clickstream, and behavioral datasets for a wider
perspective
• As a result, security analysts could see patterns
of exfiltration and infiltration, and iterate to
details without IT’s help

26. Platfora in Financial Services
• Retail
• 360 degree view of customer
• Marketing -Identify relevant customers for marketing campaigns - cross/up-sells
• Digital Marketing – consolidation of channel analysis
• Investment Banking
• Identify common customer investment/product behaviors and build strategies to leverage insights
• Identify/track trends in stock performance
• Risk & Fraud
• Comprehensive view of enterprise risk profile - Financial Risk (Credit, Market, Liquidity) Operational Risk
(Internal Audit, Vendor, Systems, Human Capital)
• Potential Fraud identification
• IT Management & Cyber Security
• Track IT events over time by user directly from logs
• Analyze threat detection data for anomalies or outliers
• Malicious email identification and threat resolution

27. 27
DEMO

28. © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Next Steps…
Download the Hortonworks Sandbox
Learn Hadoop
Build Your Analytic App
Try Hadoop 2
More about Platfora & Hortonworks
http://hortonworks.com/partner/platfora/
Contact us: events@hortonworks.com