Hortonworks and Red Hat Webinar_Sept.3rd_Part 1

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An Open Source Modern Data Architecture
…with Red Hat and Apache Hadoop
We do Hadoop.

Your speakers…
John Kreisa (@marked_man), VP Strategic Marketing, Hortonworks
Rob Cardwell, VP Middleware Technologies, Red Hat
Syed Rasheed, Sr. Solution Marketing Manager, Red Hat

Topics
• Poll – Where are you on your Hadoop Journey?
• Why an open source Modern Data Architecture?
• Hortonworks and Red Hat partnership for the open MDA
• Open source MDA roadmap

Poll: Where are you in your Hadoop journey?
1. Researching our options
2. Currently evaluating some software
3. Deep in a trial
4. What’s Hadoop?

Big Data Market Trends & Projections
Big
Data
Explosion
% by which org’s
leveraging modern info
management systems
outperform peers by 2015
85%
from new
data types
ñ
Hadoop
enabled
DBMS’s
50x
data growth 2010 to
2020
1 Zettabyte (ZB)
=
1 Billion TBs
15x
growth rate of
machine generated
data by 2020
The US has 1/3 of the world’s data
Big Data is 1 of 5 US GDP Game Changers $325 billion
incremental annual GDP from big data analytics in retail and manufacturing by
2020

A data architecture under pressure from new data
DATA SYSTEM APPLICATIONS
Business
Analytics
Custom
Applications
RDBMS EDW MPP
Packaged
Applications
• 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,…)
New Data Types
…and difficult to
manage new data

Hadoop within an emerging Modern Data Architecture
Batch Interactive Real-Time
HDFS
(Hadoop Distributed File System)
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
Geoloca9on
Sensor
&
Machine
Server
Logs
Unstructured
DATA SYSTEM APPLICATIONS
Business
Analytics
Custom
Applications
Packaged
Applications
RDBMS EDW MPP YARN: Data Operating System
1 ° ° ° ° ° ° ° ° °
° ° ° ° ° ° ° ° ° N

Hadoop: typically used for new analytic applications
SCALE SCOPE
New Analytic Apps
New types of data
LOB-driven

Clickstream
Capture and
analyze website
visitors’ data trails
and optimize your
website
Sensors
Discover patterns in
data streaming
automatically from
remote sensors and
machines
Server Logs
Research logs to
diagnose process
failures and prevent
security breaches
Hadoop Value: New types of data
Sentiment
Understand how
your customers
feel about your
brand and products
– right now
Geographic
Analyze location-based
data to
manage operations
where they occur
Unstructured
Understand patterns
in files across
millions of web
pages, emails, and
documents

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 ✔

Hadoop incrementally delivers a ‘Data Lake’
A Modern Data Architecture/Data Lake
SCALE SCOPE
RDBMS
MPP
EDW
New Analytic Apps
New types of data
LOB-driven
Governance
& Integration
Security
Operations
Data Access
Data Management
Data Lake
An architectural shift in the
data center that uses Hadoop
to deliver deeper insight across
a large, broad, diverse set of
data at efficient scale

HDP is deeply integrated in the data center
YARN
DEV
&
DATA
TOOLS
OPERATIONAL
TOOLS
INFRASTRUCTURE
SOURCES
EXISTING
Systems
Clickstream
Web
&Social
Geoloca9on
Sensor
&
Machine
Server
Logs
Unstructured
DATA SYSTEM
RDBMS
EDW
MPP
HANA
APPLICATIONS
BusinessObjects BI
HDP 2.1
Governance
& Integration
Security
Operations
Data Access
Data Management
• Enables millions of JBoss
developers to quickly build
applications with Hadoop
• Simplifies deployment of
Hadoop on OpenStack
• Develops and deploys
Apache Hadoop as
integrated components of
the open modern data
architecture

Rob Cardwell, VP Middleware Technologies
Red Hat

Companies strengthen relationship to bring Enterprise
Apache Hadoop to the open modern data architecture
• Engineering alignment
• Corporate alignment
• Field alignment

Engineering Collaboration Benefits
Integration with JBoss Data
Virtualization
Enable agile Big Data Hadoop integration with existing enterprise
assets and maximize universal data utilization to enable self-service
analytics
Integration with multiple Red Hat JBoss
Middleware product family
Enables millions of JBoss developers to quickly build applications with
Hadoop
Integration with Red Hat Storage Enables Hadoop to use Red Hat Storage secure resilient storage pool
for data applications
Integration with Red Hat Enterprise
Linux OpenStack Platform
Simplifies automated deployment of Hadoop on OpenStack
Integrated with Red Hat Enterprise
Linux and OpenJDK
Develop and deploy Apache Hadoop as an integrated component for
multiple deployment scenarios

Red Hat + Hortonworks
Delivering Value for both Business and IT organizations
Business analysts and users
Consume big data using
existing tools and skills
Application developers Easily
build new big data analytical
applications based on Hadoop
and existing sources
Enterprise architects Agile big
data integration and creation of
dynamic data supply chain to
maximize data utilization and
analytics at scale
IT Operations Enable Apache
Hadoop as an integrated,
complementary component of
the operational architecture

Deliver Open Source Modern Data Architecture
• A deeper strategic alliance
– Engineer solutions for seamless customer experience
– Joint go to market activities
– Integrated customer support
• Available now
– HDP on Red Hat Storage beta program
– Red Hat JBoss Data Virtualization with HDP
– HDP on Red Hat Enterprise Linux with OpenJDK

Syed Rasheed, Sr. Solution Marketing Manager
Red Hat

Information & Agility Gap
Only 28%
Users have any meaningful data access
Over 70% BI project efforts lies in the finding and
integration of source data
Decision-makers Are Demanding
Improved Use Of Data And Analytics
• Improve the use of data and analytics to
improve business 72% decisions and outcomes
66% • Identify new ways IT can better support
business/marketing objectives
56% • Improve IT project delivery performance
Gartner
CIO
Agenda
Report
2013
Forrester
Informa9on
Fabric
3.0
August
8,
2013

Data Challenges Getting Bigger…
NoSQL
Hive
MapReduce
HDFS
HBase
Storm
Spark

Make Big Data Accessible for Everyone

Data Supply and Integration Solution
Data Virtualization sits in front of multiple data
sources and
ü allows them to be treated a single source
ü delivering the desired data
ü in the required form
ü at the right time
ü to any application and/or user.
THINK VIRTUAL MACHINE FOR DATA

Easy Access to Big Data
Hive
• Reporting tool accesses the
data virtualization server via rich
SQL dialect
• The data virtualization server
translates rich SQL dialect to
HiveQL
• Hive translates SQL to
MapReduce
• MapReduce runs MR job on big
data
MapReduce
HDFS
Analytical
Reporting
Tool
Data
Virtualization
Server
Hadoop
Big Data

Different Users Different Views of Big Data
Hive
• Logical tables with different forms
of aggregation
• Logical tables containing extra
derived data
• Logical tables with filtered data
• All reports/users share the same
specifications
MapReduce
HDFS

Caching the Big Data
Hive
• Caches to speed up interactive
reporting
• Caches to create a consistent
view of big data
• Different caches for different
reports
MapReduce
HDFS

Integration of Big Data with “Small Data”
Database Server Hive Application
• Integrating small data with
big data is easy
• Integration specifications
can be shared or be
developed for individual
reports
MapReduce
HDFS

Security and Big Data
• Hadoop security is file-based
• Data virtualization can offer finer-grained security
• JBoss Data Virtualization can offer table, row,
column, and value level security on big data
• Works in conjunction with other SQL-on-Hadoop
implementations

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 small data
• Sharing of integration specifications
• Collaborative development on big data
• Fine-grained security of big data
• Speedy delivery of reports on big data
You Need A Data Virtualization Strategy To Avoid Falling Behind
“Without a data virtualization strategy, you risk knowing less about your customer, delivering fewer
real-time business insights, losing competitive advantage, and spending more to address data
challenges.
Informa9on
Fabric
3.0
August
8,
2013

Making it Easier for Enterprises to Harness the Power Of Big Data
• Integrating Hadoop into
existing information
infrastructure.
• Building enterprise-grade,
data-centric applications with
Hadoop.
• Operationalizing Hadoop and
deliver high quality services
around it.

Thank you!

Next Steps...
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Hortonworks and Red Hat Webinar_Sept.3rd_Part 1

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