This document summarizes a presentation about Hortonworks and how it can help companies transform their businesses with big data and Hortonworks' Hadoop distribution. Hortonworks is the sole distributor of an open source, enterprise-grade Hadoop distribution called Hortonworks Data Platform (HDP). HDP addresses enterprise requirements for mixed workloads, high availability, security and more. The presentation discusses how Hortonworks enables interoperability and supports customers. It also provides an overview of how Pactera can help clients with big data implementation, architecture, and analytics.

1. CONSULTING SOLUTIONS OUTSOURCING
PARTNER FOR A NEW
ERA
Transform Your Business with
Big Data and Hortonworks
Tom Kersnick – Pactera – Director Big Data Solutions
Robby Richardson – Hortonworks – Enterprise Account Manager

2. Topics
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2 Who is Hortonworks?
3 Hortonworks HDP: Enterprise Hadoop Distribution
4
5 Pactera Intro
6 Big Data Initiatives
Hadoop 2.0: The Enterprise Generation
1 Hortonworks Intro
2

3. Hortonworks Snapshot
• We distribute the only 100%
Open Source Enterprise
Hadoop Distribution:
Hortonworks Data Platform
• We engineer, test & certify HDP
for enterprise usage
• We employ the core architects,
builders and operators of
Apache Hadoop
• We drive innovation within
Apache Software Foundation
projects
• We are uniquely positioned to
deliver the highest quality of
Hadoop support
• We enable the ecosystem to
work better with Hadoop
Develop Distribute Support
We develop, distribute and support
the ONLY 100% open source
Enterprise Hadoop distribution
Endorsed by Strategic Partners
Headquarters: Palo Alto, CA
Employees: 200+ and growing
Investors: Benchmark, Index, Yahoo
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4. Rapid Customer Growth
4© Pactera. Confidential. All Rights Reserved. 4

5. Hortonworks HDP: Enterprise Hadoop 1.x Distribution
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OS Cloud VM Appliance
PLATFORM
SERVICES
HADOOP
CORE
Enterprise Readiness
High Availability, Disaster Recovery,
Security and Snapshots
HORTONWORKS
DATA PLATFORM (HDP)
OPERATIONAL
SERVICES
DATA
SERVICES
HIVE
(HCATALOG)
PIG HBASE
OOZIE
AMBARI
HDFS
MAP REDUCE
Hortonworks
Data Platform (HDP)
Enterprise Hadoop
• The ONLY 100% open source and
complete distribution
• Enterprise grade, proven and
tested at scale
• Ecosystem endorsed to ensure
interoperability
SQOOP
FLUME
NFS
LOAD &
EXTRACT
WebHDFS
5

6. Hadoop 2.0… The Enterprise Generation
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Business Value
Big Data
Transactions,
Interactions,
Observations
Single Platform
Multiple Use
BATCH
INTERACTIVE
ONLINE
1.0 Architected for the Large Web Properties
2.0 Architected for the Broad Enterprise
Enterprise Requirements Hadoop 2.0 Features
Mixed workloads YARN
Interactive Query Hive on Tez
Reliability Full Stack HA
Point in time Recovery Snapshots
Multi Data Center Disaster Recovery
ZERO downtime Rolling Upgrades
Security Knox Gateway
6

7. HDP: Enterprise Hadoop 2.0 Distribution
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OS/VM Cloud Appliance
PLATFORM
SERVICES
HADOOP
CORE
Enterprise Readiness
High Availability, Disaster
Recovery, Rolling
Upgrades, Security and
Snapshots
HORTONWORKS
DATA PLATFORM (HDP)
OPERATIONAL
SERVICES
DATA
SERVICES
HIVE &
HCATALOG
PIG HBASE
HDFS
MAP
Hortonworks
Data Platform (HDP)
Enterprise Hadoop
• The ONLY 100% open source and
complete distribution
• Enterprise grade, proven and
tested at scale
• Ecosystem endorsed to ensure
interoperability
SQOOP
FLUME
NFS
LOAD &
EXTRACT
WebHDFS
KNOX*
OOZIE
AMBARI
FALCON*
YARN*
TEZ* OTHERREDUCE
7

8. Seamless Interoperability with Microsoft Tools
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• Integrated with Microsoft
tools for native big data
analysis
» Bi-directional connectors for SQL
Server and SQL Azure through
SQOOP
» Excel ODBC integration through
Hive
• Addressing demand for
Hadoop on Windows
» Ideal for Windows customers with
Hadoop operational experience
• Enables most common
Hadoop workloads in the
Enterprise
» Data refinement and ETL offload
for high-volume data landing
» Data exploration for discovery of
new business opportunities
» Data enrichment for fined tuned
delivery and recommendation
engines
APPLICATIONSDATASYSTEMS
Microsoft Applications
HORTONWORKS
DATA PLATFORM
For Windows
DATASOURCES
MOBILE
DATA
OLTP, PO
S
SYSTEMS
Traditional Sources
(RDBMS, OLTP, OLAP)
New Sources
(web logs, email, sensor data, social media)
8

9. Transferring Our Hadoop Expertise to You
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The expert source for
Apache Hadoop training & certification
• World class training programs designed to help you learn
fast
• Role-based hands on classes with 50% lab time
• Certification to demonstrate Hadoop Expertise in
Development and Administration
• Expert consulting services
• Programs designed to transfer knowledge
• Industry leading Hadoop Sandbox
• Free download
• Fastest way to learn Apache Hadoop
• Personal, portable Hadoop environment
9

10. Hortonworks Summary
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• Leading the Innovation in Core Hadoop
• Addressing the requirements for Enterprise usage
• Enabling interoperability of the ecosystem
• No lock-in. 100% Open Source.
• Best in industry support with flexible pricing model
• Find out moreworks.com
» www.hortonworks.com/hadoop-training/
» www.hortonworks.com/sandbox
10

11. Big Data is Critical
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Challenges to Using Big Data
Given that nearly less than one-third of businesses are in the dark about their
available data, it makes sense that silos are the primary hurdle in using this
information.
Lack of
sharing data is
an obstacle to
measuring
marketing ROI
Not using data
effectively to
personalize
marketing
communications
Not able to
link data
together at
the individual
customer level
Data collected
infrequently or
not quickly
enough
Too little or no
customer/
consumer data
51% 45% 42% 39% 29%
11

12. What Initiatives Are Using Big Data
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13. Keys to a Successful Big Data Initiative
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Define the Impact
• Short term VS. Long term measures
What cannot be answered today?
• This is your starting point
Create User Centric Internal | External Applications
• Decision support framework
Predicting the Consumer
• Algorithms, Models, Testing, and
More Testing!
13

14. Obstacles to Define Big Data ROI
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Not enough skilled resources for adaptation
• Advance competencies
Traditional IT Architectures cause limitations
• Identifying the right technologies
• Adapting to particular needs
• Assemble business use cases
• Silos
Optimizing Solutions
• Strong internal use cases
• Inability to effectively automate data
14

15. Solution Architecture using Multiple Ecosystems
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incoming
outgoing
Real Time In-Memory
Solution
EDW
Hadoop
Sand
box
2
3
4
7
8
9
6
5
Models
Algorithms
Simulations
1. Data Feeds into a Real-Time Memory solution that will ingest data into EDW, Hadoop, and other platforms as
mobile, API’s, etc.
2. ELT streaming into In-Memory Solution to provide visibility to Real-Time Social, Mobile, and Shell approaches to
Algorithms, Models, and Simulations
3. In-Memory Real-Time Solution such as YARN or Storm to digest data to EDW, Hadoop, Social Media, and other such
platforms.
4. EDW for Structured Information from Sources in 1.
5. Hadoop for semi-structured and unstructured data. Solution architecture including Sand Box availability.
6. Shell UI Interfaces utilizing data from Real-Time in memory solution as well as EDW, Hadoop, etc. for
Models, Algorithms, and Simulations.
7. Structured and Unstructured Reporting in reporting interfaces
8. Deep Dive analytics in Hadoop and Real-time Streaming
9. Real-Time customer interaction for Social and other similar platforms.
1
15

16. Predictive
Analysis
Use Case
for Online Travel
Company
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17. Flight Cost by Variants Determination
Data Feeds utilize real-time in-memory streaming to execute matching algorithms.
Used in order to determine views within a session of certain one-way and round trip
flights viewed by users.
Predictive Analytics algorithms determine how to increase/decrease prices based on
views, market pricing, time, and availability.
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http
logs
partners
custom
incoming
outgoing
destinations
rdbms
hadoop
application
mobile
Real Time In-Memory Solution
(Storm)
17

18. Solution Architecture using YARN
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• Created to manage resource needs across all uses
• Ensures predictable performance & QoS for all apps
• Enables apps to run “IN” Hadoop rather than “ON”
» Key to leveraging all other common services of the Hadoop platform: security,
data lifecycle management, etc.
Applications Run Natively IN Hadoop
HDFS2 (Redundant, Reliable Storage)
YARN (Cluster Resource Management)
BATCH
(MapReduce)
INTERACTIVE
(Tez)
STREAMING
(Storm, S4,…)
GRAPH
(Giraph)
IN-MEMORY
(Spark)
HPC MPI
(OpenMPI)
ONLINE
(HBase)
OTHER
(Search)
(Weave…)
18

19. Pactera Big Data Capability
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Big Data Solution Architecture
 In-Memory Solutions
 Scalable Distributed Platforms
Next Generation Analytics
 Models, Algorithms, and Simulations
 Visualization
Improving Operational Ability
 Help companies drive more operational efficiencies from existing
investments.
 Moving from the realm of data scientists into everyday business transactions
and encounters.
New Business Processes
 Impact on both customer intelligence and operational efficiency by making
everything immediately actionable.
 Armed with immediate decision-making capability and intelligence,
companies will be able to implement new business processes that will
change how business is done.
 We ask the Right Questions
19

20. How Pactera can help with Big Data
Implementation and Architecture
Benchmark and Monitoring
Implementation and Architecture
POC (2-4 Weeks)
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Executive Workshop
Strategies, Planning, and Expectations
• Big Data strategy on what tomorrow will look like
• Using Big Data to establish market dominance
• Big Data project takeaways
• Roadblocks to implementing Big Data analytics
• Defining an ROI for Big Data
• Getting the right ROI on Big Data
Workshop
(4 Hours)
Proof of Concept
(2-4 Weeks)
Projects:
•Benchmark & Monitoring
•Integrations & Migrations
•Implementation & Architecture
•Project Management
•Analytics
•Reporting
Technical Workshop
End-To-End Management
• System tuning/auto-tuning and configuration management
• Dealing with both structured and unstructured data
• Monitoring, diagnosis, and automated behavior detection
Solution Architecture
• Processor, memory, and system architectures for data analysis
• Benchmarks, metrics, and workload characterization for big
data
• Availability, fault tolerance and recovery issues
• Data management and analytics for vast amounts of
unstructured data
20

21. © Pactera. Confidential. All Rights Reserved.
Thank You
Tom Kersnick
tom.kersnick@pactera.com
Robby Richardson
rrichardson@hortonworks.com