You’re hearing a lot about big data these days. And big data and the technologies that store and process it, like Hadoop, aren’t just new data silos. You might be looking to integrate big data with existing enterprise information systems to gain better understanding of your business. You want to take informed action. During this session, we’ll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data. You’ll learn how Red Hat JBoss Data Virtualization: Can help you integrate your existing and growing data infrastructure. Integrates big data with your existing enterprise data infrastructure. Lets non-technical users access big data result sets. We’ll also provide typical uses cases and examples and a demonstration of the integration of Hadoop sentiment analysis with sales data.

1. GAIN BETTER INSIGHT FROM BIG DATA
USING JBOSS DATA VIRTUALIZATION
Syed Rasheed
Solution Manager
Red Hat Corp.
Kenny Peeples
Technical Manager
Red Hat Corp.
Kimberly Palko
Product Manager
Red Hat Corp.

2. AGENDA
Demystifying Big Data
Data Virtualization: Making Big Data Available to Everyone
Red Hat Big Data Strategy and Platform
Real World Customer Example using Red Hat Big Data Platform
Demo
Roadmap
Q&A

3. DO WE AGREE ON WHAT BIG DATA IS?

4. Source: http://blogs.ifsworld.com/2013/02/how-will-big-data-influence-your-finance-team/

5. IT’S ALL ABOUT GAINING BUSINESS INSIGHTS
Improve product development
Optimize business processes
Improve customer care
Improve customer lifetime value
Personalize products
Competitive intelligence
…

6. INFORMATION AND AGILITY GAP
OverOver 70%70%BI project efforts lies in
Data Integration – finding and
identifying source data
OnlyOnly 28%28%Users have any meaningful data
access

7. DATA CHALLENGES GETTING BIGGER FOR USERS
NoSQL
Hive
MapReduce
HDFS
Pig
Jaql
Flume
Storm
HBase

8. RED HAT’S BIG DATA STRATEGY
Reduce Information Gap thru cost effectively making ALL
data easily consumable for analytics
Data
Analytics
Data to Actionable Information Cycle

9. BIG DATA FOR EVERYONE

10. EASY ACCESS TO BIG DATA
BI Reports & Analytics
Hive
MapReduce
HDFS
Analytical Reporting Tool
Data Virtualization Server
Hadoop
Big Data
1. Reporting tool accesses the data
virtualization server via rich SQL
dialect
2. The data virtualization server
translates rich SQL dialect to HiveQL
3. Hive translates SQL to MapReduce
4. MapReduce runs MR job on big data

11. TURN FRAGMENTED DATA INTO ACTIONABLE INFORMATION
ConnectConnect
ComposeCompose
ConsumeConsume
BI Reports & Analytics
Mobile Applications
SOA Applications & PortalsESB, ETL
Native Data ConnectivityNative Data Connectivity
Standard based Data Provisioning
JDBC, ODBC, REST, SOAP, OData
Standard based Data Provisioning
JDBC, ODBC, REST, SOAP, OData
Design ToolsDesign Tools
DashboardDashboard
OptimizationOptimization
CachingCaching
SecuritySecurity
MetadataMetadata
Hadoop NoSQL Cloud Apps Data Warehouse
& Databases
Mainframe
XML, CSV
& Excel Files
Enterprise Apps
Siloed &
Complex
Virtualize
Transform
Federate
Easy,
Real-time
Information
Access
Unified Virtual Database / Common Data Model
Data Transformations
Unified Virtual Database / Common Data Model
Data Transformations

12. BENEFITS OF DATA VIRTUALIZATION ON BIG DATA
Enterprise democratization of big data
Any reporting or analytical tool can be used
Easy access to big data
Seamless integration of big data and existing data assets
Sharing of integration specifications
Collaborative development on big data
Fine-grained of security big data
Increased time-to-market of reports on big data

13. CONVERGENCE OF FOUR DATA TRENDS

14. COMPREHENSIVE MIDDLEWARE PLATFORM
CAPTURE, PROCESS AND INTEGRATE BIG DATA VOLUME, VELOCITY, VARIETY
Hadoop
Data Integration
JBoss Data Virtualization
Data Integration
JBoss Data Virtualization
In-memory Cache
JBoss Data Grid
In-memory Cache
JBoss Data Grid
BI Analytics
(historical, operational, predictive)
BI Analytics
(historical, operational, predictive)
SOA Composite ApplicationsSOA Composite Applications
Messaging and Event Processing
JBoss A-MQ and JBoss BRMS
J
Messaging and Event Processing
JBoss A-MQ and JBoss BRMS
J
Structured DataStructured Data Streaming DataStreaming Data Semi-Structured DataSemi-Structured Data
RedHatStorage
RedHatEnterpriseLinux&Virtualization
Capture&ProcessIntegrate&Analyze

15. RED HAT BIG DATA PLATFORM

16. EXAMPLES:
RED HAT BIG DATA PLATFORM
IN THE REAL WORLD

17. BIG DATA IN THE UTILITIES
Objective:
Combine data from smart meters on homes with data from electricity generation and transmission and
make it available to power providers
Problem:
The original smart grid project looked only at reading information from the meters on houses and now this
data needs to be combined with generation and transmission data in a cost-effective way
The data points are all over the place: sensors on the lines, in the field, homes, etc.
The information must be accessible to multiple power providers through a common interface
Solution:
Use Messaging to collect data from a variety of sources and route it to a CEP for initial filtering. Process
with Hadoop map/reduce and BRMS and distribute data to Data Virtualization to be combined with other
sources and consumed with BI tools, and/or to JDG for in-memory data caching and/or send to archive.

18. SMART GRID
TransmissionTransmission GenerationGeneration ConsumerConsumer
RegulatoryRegulatory UsersUsers
Collector
Sensors
Collector
Sensors Local
Data
Store
Local
Data
Store
Collector
Scada
Collector
Scada Local
Data
Store
Local
Data
Store
Collector
Meter
Collector
Meter Local
Data
Store
Local
Data
Store
Adaptor
Rules
Adaptor
Rules
Sensor
Adaptor
Sensor
Adaptor
Routing
Function
Routing
Function
Normalization /
MapReduce
Normalization /
MapReduce
PM Regional
Translator /
Scheduler
PM Regional
Translator /
Scheduler
Offline
Storage
Offline
Storage
Data
Virtualization
Data
Virtualization CacheCache
AuthenticationAuthentication PresentationPresentation REST ExposureREST Exposure
Element Connection
Tier
Data Adaptation
& Routing Tier
Normalized Data
Tier
Data
Tier
API Exposure
&Portal Tier
ComposeCompose
PM Data Schedule
PM Data Reports
Rules Creation
/ Updates
PM Admin
NoSQL-Cassandra

19. RETAIL CUSTOMER USE CASE
GAIN BETTER INSIGHT FOR INTELLIGENT INVENTORY MANAGEMENT
Objective:
Right merchandise, at right time and price
Problem:
Cannot utilize social data and sentiment analysis
with their inventory and purchase management
system
Solution:
Leverage JBoss Data Virtualization to mashup
Sentiment analysis data with inventory and
purchasing system data. Leveraged BRMS to
optimize pricing and stocking decisions.
Consume
Compose
Connect
Analytical Apps
JBoss Data Virtualization
Hive
Inventory
Databases
Purchase Mgmt
Application
Sentiment
Analysis
JBoss
BRMS
Data Driven
Decision
Management

20. DEMOS
LUCIDWORKS, JBOSS DATA
VIRTUALIZATION AND RED HAT
STORAGE

21. ABOUT LUCIDWORKS
Employs 40% of the “committers” for Lucene/Solr
Makes 50% - 70% of the enhancements to each release of
Lucene/Solr
Only company to offer Open Source and Open Core Search Solutions

22. LUCENE/SOLR: ENABLING BETTER, DATA-DRIVEN DECISIONS

23. LUCIDWORKS DEMONSTRATION
• LucidWorks/Solr to provide full
text search and statistics
• Data Virtualization provides
the data through Teiid JDBC
driver and pulls the data from
Hive/Hadoop, CSV File, XML
File
• Red Hat Storage provides the
Enterprise Data Repository

24. DEMONSTRATION ARCHITECTURE

25. DEMOS
HORTONWORKS AND JBOSS DATA
VIRTUALIZATION

26. ABOUT HORTONWORKS
Founded in 2011 by 24 engineers from the original Yahoo! Hadoop
development and operations team
Hortonworks drive innovation in the open exclusively via the Apache
Software Foundation process
Hortonworks is responsible for around 50% of core code base
advances to Apache Hadoop

27. HORTONWORKS DATA PLATFORM 2 SANDBOX
Enterprise Ready YARN, the Hadoop Operating System
Stinger Phase 2; Interactive SQL Queries at Petabyte Scale
Reliable NoSQL IN Hadoop with Hbase
Technical Specs Component Version
Apache Hadoop 2.2.0
Apache Hive 0.12.0
Apache HCatalog 0.12.0
Apache HBase 0.96.0
Apache ZooKeeper 3.4.5
Apache Pig 0.12.0
Apache Sqoop 1.4.4
Apache Flume 1.4.0
Apache Oozie 4.0.0
Apache Ambari 1.4.1
Apache Mahout 0.8.0
Hue 2.3.0

28. HORTONWORKS
DEMONSTRATION
Objective:
Secure data according to Role for row
level security and Column Masking
Problem:
Cannot hide region data such as patient
data from region specific users
Solution:
Leverage JBoss Data Virtualization to
provide Row Level Security and Masking
of columns
Consume
Compose
Connect
DV Dashboard to analyze the aggregated data by User
Role
JBoss Data Virtualization
Hive
SOURCE 1: Hive/Hadoop in the HDP
contains US Region Data
SOURCE 2: Hive/Hadoop in the HDP
contains EU Region Data
Hive

29. HORTONWORKS
DEMONSTRATION
Objective:
Determine if sentiment data from the first
week of the Iron Man 3 movie is a
predictor of sales
Problem:
Cannot utilize social data and sentiment
analysis with sales management system
Solution:
Leverage JBoss Data Virtualization to
mashup Sentiment analysis data with
ticket and merchandise sales data on
MySQL into a single view of the data.
Consume
Compose
Connect
Excel Powerview and DV Dashboard to
analyze the aggregated data
JBoss Data Virtualization
Hive
SOURCE 1: Hive/Hadoop contains twitter
data including sentiment
SOURCE 2: MySQL data that includes
ticket and merchandise sales

30. DEMONSTRATION SYSTEM REQUIREMENTS
• JDK
– Oracle JDK 1.6, 1.7 or OpenJDK 1.6 or 1.7
• JBoss Data Virtualization v6 Beta
– http://jboss.org/products/datavirt.html
• JBoss Developer Studio
– http://jboss.org/products
• JBoss Integration Stack Tools (Teiid)
– https://devstudio.jboss.com/updates/7.0-development/integration-stack/
• Slides, Code and References for demo
– https://github.com/DataVirtualizationByExample/Mashup-with-Hive-and-MySQL
• Hortonworks Data Platform (A VM for testing Hive/Hadoop)
– http://hortonworks.com/products/hdp-2/#install
• Red Hat Storage
– http://www.redhat.com/products/storage-server/

37. JBOSS DATA VIRTUALIZATION
PRODUCT ROADMAP AND BIG DATA

38. WHAT COMING: JBOSS DATA VIRTUALIZATION 6.1

39. BENEFITS OF DATA VIRTUALIZATION ON BIG
DATA
Enterprise democratization of big data
Any reporting or analytical tool can be used
Easy access to big data
Seamless integration of big data and existing data assets
Sharing of integration specifications
Collaborative development on big data
Fine-grained of security big data
Increased time-to-market of reports on big data

40. WHY RED HAT FOR BIG DATA?
Transform ALL data into actionable information
Cost Effective, Comprehensive Platform
Community based Innovation
Enterprise Class Software and Support
Data
Analytics
Data to Actionable Information Cycle

41. THANK YOU
Q & A