jdv big data webinar v2

GAIN BETTER INSIGHTS FROM BIG DATA

USING

RED HAT JBOSS DATA VIRTUALIZATION

Syed Rasheed Product Marketing ManagerKenny Peeples Technical Marketing ManagerRed Hat CorporationDecember 4th, 2013

2 RED HAT CONFIDENTIAL

Red Hat is…

“By running tests and executing numerous examples for specific teams, we were able to prove […] not only would the solution work, but it will perform better & at a fraction of the costs.”

MICHAEL BLAKE, Director, Systems & Architecture


Agenda

● Data challenges getting bigger● Red Hat Big Data Strategy and Platform● Data Virtualization Overview ● Customer Use Case for Big Data integration using Data

Virtualization● Demo● Q&A


Poll Question #1

● What are your plans regarding usage of Hadoop technology at your company? – No plans– Under consideration– Under development– Project level deployment– Enterprise level deployment


Poll Question #2

● What are your plans regarding usage of Data Virtualization technology at your company? – No plans– Under consideration– Under development– Project level deployment– Enterprise level deployment


Data Driven Economy

Data is becoming the new raw material of business: an economic input almost on a par with capital and labor. “Every day I wake up and ask, ‘how can I flow data better, manage data better, analyze data better?”

CIO - Wal-Mart


Data Challenges Getting BiggerBig Data, Cloud, and MobileExisting Data Integration approaches are not sufficient● Extracting and moving data adds latency and cost

● Every project solves data access and integration in a different way

● Solutions are tightly coupled to data sources

● Poor flexibility and agility

BI Reports Operational Reports

Enterprise Applications

SOA Applications

Mobile Applications

Hadoop NoSQL Cloud Apps Data Warehouse & Databases

Mainframe XML, CSV& Excel Files

Enterprise Apps

Integration Complexity

Constant Change

Siloed &Complex

How to align?


Business ObjectiveTurn Data into Actionable Information

Over 70%BI project efforts lies in the integration of source data

Only 28%Users have any meaningful

data access Reduce costs for finding and accessing highly fragmented data

Improve time to market for new products and services by simplifying data access and integration

Deliver IT solution agility necessary to capitalize on constantly changing market conditions

Transform fragmented data into actionable information that delivers competitive advantage


Red Hat’s Big Data Strategy

● Reduce Information Gap thru cost effectively making ALL data easily consumable for analytics

Capture Process Integrate

Data

Analytics

Data to Actionable Information Cycle


Red Hat Big Data Platform

HadoopIntegration

JBoss DataVirtualization

RHEL Platform Integration

& Optimization

Hadoop On Red Hat Storage

Storage

Cloud /VirtualizationMiddleware

Hadoop On

OpenStack

Platform

Hadoop

on

FedoraApache Hadoop

FedoraBig Data SIG

Hadoop Distributions




& Optimization


Storage

Cloud /Virtualization

MiddlewareHadoop

On OpenStack

HadoopIntegration


Platform

Hadoop

on

FedoraApache Hadoop

FedoraBig Data SIG



What does Data Virtualization software do?Turn Fragmented Data into Actionable Information

Data Virtualization software virtually unifies data spread across various disparate sources; and makes it available to applications as a single consolidated data source.

The data virtualization software implements 3 steps process to bridge data sources and data consumers:

● Connect: Fast access to data from diverse data sources

● Compose: Easily create unified virtual data models and views by combining and transforming data from multiple sources.

● Consume: Expose consistent information to data consumers in the right form thru standard data access methods.

Virtual Consolidated Data Source

BI Reports

Data Virtualization Software• Consume• Compose• Connect

SAP Salesforce.comOracle DW Hadoop

Siloed & Complex

VirtualizeAbstractFederate

Easy,Real-time

InformationAccess

SOA Applications

DATA CONSUMERS

DATA SOURCES


Turn Fragmented Data into Actionable Information

Connect

Compose

Consume

Unified Customer View

Unified Product View

Unified Supplier View

BI Reports & AnalyticsMobile Applications

SOA Applications & Portals

Unified Virtual Database / Common Data Model

ESB, ETL

Native Data Connectivity

Standard based Data ProvisioningJDBC, ODBC, SOAP, REST, OData

JBos

s D

ata

Virt

ualiz

ation

Dat

a Co

nsum

ers

Dat

a So

urce

s

Design Tools

Dashboard

Optimization

Caching

Security

Metadata

Hadoop NoSQL Cloud Apps Data Warehouse & Databases Mainframe

XML, CSV& Excel Files

Enterprise Apps

Siloed & Complex

VirtualizeAbstractFederate

Easy,Real-time

InformationAccess


JBoss Data Virtualization:Supported Data SourcesEnterprise RDBMS:• Oracle • IBM DB2 • Microsoft SQL Server• Sybase ASE• MySQL• PostgreSQL• Ingres

Enterprise EDW:• Teradata • Netezza • Greenplum

Hadoop:• Apache• HortonWorks• Cloudera• More coming…

Office Productivity:• Microsoft Excel • Microsoft Access• Google Spreadsheets

Specialty Data Sources:• ModeShape

Repository• Mondrian• MetaMatrix• LDAP

NoSQL:• JBoss Data Grid• MongoDB • More coming…

Enterprise & Cloud Applications:• Salesforce.com• SAP

Technology Connectors:• Flat Files, XML Files,

XML over HTTP• SOAP Web Services• REST Web Services• OData Services


Key New Features and Capabilities● Data connectivity enhancements

– Hadoop Integration (Hive – Big Data), – NoSQL (MongoDB – Tech Preview) and JBoss Data Grid – Odata support (SAP integration)

● Developer Productivity improvements – New VDB Designer 8 and integration with JBoss Developer Studio v7– Enhanced column level security, – VDB import/reuse, and native queries

● Simplify deployment and packaging – Requires JBoss EAP only; included with subscription– Remove dependency with SOA Platform

● Business Dashboard– New rapid data reporting/visualization capability


JBoss Data Virtualization – Use Cases

Self-Service Business Intelligence

The virtual, reusable data model provides business-friendly representation of data, allowing the user to interact with their data without having to know the complexities of their database or where the data is stored and allowing multiple BI tools to acquire data from centralized data layer. Gain better insights from Big Data using JBoss Data Virtualization to integrate with existing information sources.

360◦ Unified View

Deliver a complete view of master & transactional data in real-time. The virtual data layer serves as a unified, enterprise-wide view of business information that improves users’ ability to understand and leverage enterprise data.

Agile SOA Data Services

A data virtualization layer deliver the missing data services layer to SOA applications. JBoss Data Virtualization increases agility and loose coupling with virtual data stores without the need to touch underlying sources and creation of data services that encapsulate the data access logic and allowing multiple business service to acquire data from centralized data layer.

Regulatory Compliance

Data Virtualization layer deliver the data firewall functionality. JBoss Data Virtualization improves data quality via centralized access control, robust security infrastructure and reduction in physical copies of data thus reducing risk. Furthermore, the metadata repository catalogs enterprise data locations and the relationships between the data in various data stores, enabling transparency and visibility.


Retail Customer Use CaseGain Better Insight from Big Data 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.

ConsumeComposeConnect

Analytical Apps

JBoss Data Virtualization

Hive

Inventory Databases

Purchase Mgmt Application

SentimentAnalysis

JBoss BRMS

Data Driven Decision

Management

Big Data integration use case


Better Together - Big Data and Data VirtualizationHadoop not another Silo - Customers Combine Multiple Technologies

● Combine structured and unstructured analysis– Augment data warehouse with additional external sources, such as

social media

● Combine high velocity and historical analysis– Analyze and react to data in motion; adjust models with deep

historical analysis

● Reuse structured data for analysis– Experimentation and ad-hoc analysis with structured data


Better Together - Big Data and Data VirtualizationCapture, Process and Integrate Data Volume, Velocity, Variety

Hadoop

Data IntegrationJBoss Data Virtualization

In-memory CacheJBoss Data Grid

BI Analytics (historical, operational, predictive) SOA Composite Applications

Messaging and Event Processing JBoss A-MQ and JBoss BRMS

J

Structured DataStreaming

DataSemi-Structured

Data

Red Hat Storage

Red Hat Enterprise Linux &

VirtualizationCap

ture

& P

roce

ss I

nte

gra

te &

An

alyz

e


Consider...

How would your organization change…● If data were readily reusable in place rather than

requiring significant effort to build new intermediary data tiers?

● If data could be repurposed quickly into new applications and business processes?

● If all applications and business processes could get all of the information needed in the form needed, where needed and when needed?

Inconsistent, Incomplete Information

Uninformed, Delayed Decisions

Costly Business Risk and Exposure


Red Hat JBoss Middleware

Foundation

Data Integration

Application Integration

Business ProcessManagement

User Interaction

Developm

entToolsh

Managem

entTools

• JBoss EAP• JBoss Web Server• JBoss Data Grid

• JBoss Data Virtualization

• JBoss A-MQ• JBoss Fuse• JBoss Fuse Service Works

• JBoss BRMS• JBoss BPM Suite

• JBoss Portal

•JBoss D

eveloper Studio

•JBoss O

perations Netw

ork

ACCELERATE INTEGRATE AUTOMATE

DEMOBig Data Integration using JBoss Data Virtualization


Demo Scenario

● 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.

ConsumeComposeConnect

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


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-MyS

QL

• 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/

http://jboss.org/products/datavirt.html

http://jboss.org/products

https://devstudio.jboss.com/updates/7.0-development/integration-stack/

https://github.com/DataVirtualizationByExample/Mashup-with-Hive-and-MySQL

https://github.com/DataVirtualizationByExample/Mashup-with-Hive-and-MySQL

http://hortonworks.com/products/hdp-2/#install

http://www.redhat.com/products/storage-server/


Why Red Hat for Big Data?

● Transform ALL data into actionable information– Cost Effective, Comprehensive Platform– Community based Innovation– Enterprise Class Software and Support

Capture Process Integrate

Data

Information

Data to Actionable Information Cycle



HadoopIntegration



& Optimization


Storage

Cloud /VirtualizationMiddleware

Hadoop On

OpenStack

Platform

Hadoop

on

FedoraApache Hadoop

FedoraBig Data SIG


Thank YouQ&A

jdv big data webinar v2

Technology

data spread

big data integration

bigger big data

integration of source

bridge data sources

diverse data sources

standard data access

meaningful data access