The document discusses an upcoming webinar on Big Data and SQL. It provides details on the webinar topics, speakers, and the HP Vertica Analytics Platform. The webinar will explore how HP Vertica allows users to navigate and analyze data stored in Hadoop using SQL, avoiding complex ETL processes. It will also discuss how the platform handles both query and analytical workloads and enables exploration of semi-structured data through its Flex Zone.

1. Grab some coffee and enjoy
the pre-show banter before
the top of the hour!

2. Big Data & SQL: The On-Ramp to Hadoop
The Briefing Room

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The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

4. ! Reveal the essential characteristics of enterprise software,
good and bad
! Provide a forum for detailed analysis of today’s innovative
technologies
! Give vendors a chance to explain their product to savvy
analysts
! Allow audience members to pose serious questions... and get
answers!
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The Briefing Room
Mission

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The Briefing Room
Topics
This Month: BIG DATA
May: DATABASE
June: ANALYTICS & MACHINE LEARNING
2014 Editorial Calendar at
www.insideanalysis.com/webcasts/the-briefing-room

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The Briefing Room
Big Data

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The Briefing Room
Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor

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The Briefing Room
HP Vertica
! Vertica was founded in 2005 by Michael Stonebreaker and
Andrew Palmer; it was acquired by HP in 2011
! HP Vertica Analytics Platform is a grid-based, column-oriented
database management system
! The latest release, Version 7, offers new platform
components that allow for Hadoop exploration and analysis
using SQL

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The Briefing Room
Guests
Eamon O Neill, Manager, Product Management, HP Vertica
Eamon leads the product management efforts for the HP Vertica Analytics
Platform. He has more than 15 years of high-tech product management
experience and deep knowledge of mobile applications, software defined
networking and storage, database marketing, and distributed systems. Eamon
had a founding role in the creation of the cloud services platform at BladeLogic
(now BMC Software). In addition to BMC Software, Eamon held product
management, software engineering, and business consulting roles at Hitachi
Data Systems, Unica (now IBM), and Cambridge Technology Partners.
Jeff Healey, Director of Product Marketing, HP Vertica
Jeff leads the product marketing and customer marketing efforts for the HP
Vertica Analytics Platform. Jeff has more than 15 years of high-tech marketing
experience and deep knowledge in messaging, positioning, and content
development. Jeff previously led product marketing initiatives for Axeda
Corporation, an M2M platform for sensor data and the Internet of Things. Prior
to Axeda, Jeff held product marketing, customer marketing, and lead editorial
roles at The MathWorks, Macromedia (now Adobe), Sybase (now SAP), and The
Boston Globe.

10. Big Data & SQL:
The On-Ramp to
Hadoop
With the HP Vertica Analytics Platform
Jeff Healey, Director of Product Marketing, HP Vertica
Eamon O’Neill, Director of Product Management, HP Vertica
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

11. A New Era of Accelerated Innovation
Forever changing how consumers and businesses interact, enabling new opportunities
2013 By 2020
Every 60 seconds
98,000+ tweets
695,000 status updates
11million instant messages
698,445 Google searches
168 million+ emails sent
1,820TB of data created
217 new mobile web users
Growing Internet of Things (IoT)
Pervasive
Connectivity
Explosion of
Information
Smart Device
Expansion
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 11 without notice.
40 Trillion GB(2)
10 Million(3)
… for 8 Billion(4)
(1) IDC Directions 2013: Why the Datacenter of the Future Will Leverage a Converged Infrastructure, March 2013, Matt
Eastwood ; (2) & (3) IDC Predictions 2012: Competing for 2020, Document 231720, December 2011, Frank Gens; (4)
http://en.wikipedia.org
30 Billion(1)
Devices
DATA
Mobile
Apps

12. The Time is Now
CRM ERP Data Warehouse Web Social Log Files Machine Data Semi-structured
Data Volumes
Accuracy and Insight
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 12 without notice.
Dark Data
Traditional Big Data
Enterprise Data
Unstructured

13. HP Vertica Analytics Platform
Vertica Flex Zone Vertica Enterprise
Column Store Optimizer & Execution Engine
High Availability & Redundancy
MPP Shared Nothing
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 13 without notice.
13
JSON, CEF, Delimited
Database Designer
HP ConvergedSystem 300 for Vertica
Management Console
HDFS, Hcatalog, Flume, Files
API & SDK (supports R, C++, Java)
Time
Series
Analytics
Functions
Distributed
R
SQL
ODBC
JDBC
Search
Functionality
Geospatial
&
Sentiment
Key
Value
API
HP BSM & Security
Community BI Ecosystem 3rd party apps
Marketplace

14. The Richest, Most Open SQL on Hadoop
Challenge: Extracting data from Hadoop requires complex and
brittle ETL processes
Solution: Hadoop Navigation and Analytics
Benefits:
• Navigate Hadoop data using its native catalog
• Quickly and easily load native data types from Hadoop to Vertica
• Avoid creating and maintaining time-consuming schemas
• Use the full power of HP Vertica SQL and analytics
• Choose your own Hadoop distribution
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 14 without notice.

15. The Richest, Most Open SQL on Hadoop
Challenge: Extracting Data from Hadoop requires complex and
brittle ETL processes
Solution: Hadoop Navigation and Analytics
Benefits:
• Navigate Hadoop data using its native catalog
• Quickly and easily load native data types from Hadoop to Vertica
• Avoid creating and maintaining time-consuming schemas
• Use the full power of HP Vertica SQL and Analytics
• Choose your own Hadoop distribution
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 15 without notice.

16. HP Vertica and MapR Solution
HP Vertica Analytics Platform on MapR
Optimized, interactive SQL-on-Hadoop solution for fastest value from big data
• Complete SQL-on-Hadoop Solution
• Broader Analytics Capabilities
• Lower TCO & Manageability
• Enterprise-Grade Reliability
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 16 without notice.

17. Most Complete SQL on Hadoop
HP Vertica on MapR Limited “Query on Hadoop” Options
• Full interactive ANSI SQL on Hadoop
• More complete SQL maturity
• Clients can leverage existing SQL skills
• Handling complex joins and advanced analytic
functions, query optimization, and many
concurrent users
• Certified integration with BI/visualization
environments
• Dynamic handling of mixed workloads
• Supports a limited subset of HiveQL1
– 1HiveQL is SQL-like dialect - subset of ANSI SQL
• HiveQL is not as mature as SQL
• Requires new skills
• Immature query optimization for planning
efficient joins and for processing
• Onus on customer to integrate with BI/
visualization environments
• Lack of workload management for high
number of concurrent users
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 17 without notice.

18. • Analyzing gene
variants using SNPs
and Microarray data
The problem:
The solution:
• Hadoop to find the variants
between a sample sequence
and a reference genome
• HP Vertica to determine
oncology targets
• Tools: Pipeline Pilot, Spotfire, R
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 18 without notice.
• Queries went from 5
hours to 5 minutes
• Scale to 100s of TB of
data
• More experiments =>
faster discoveries!
The value:
Accelerating Drug Discovery
Innovative Healthcare
Products Company

19. HP Vertica Flex Zone
Avoid creating and maintaining time-consuming schemas
Faster SQL querying
on semi-structured data
Auto-schematization
semi-structured data loading
Flexible parsers
for JSON and delimited data
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 19 without notice.
One-step schema
for blazing-fast performance
Load, manage, and explore semi-structured data

20. Exploring the Value of Dark Data
Leading online source for health and medical news and information
Challenges
• Takes 900 hours per year to
ingest semi-structured data
for analysis
• As requirements change,
must again “re-structure” the
data for exploration
• Must meet ever-increasing
requests for analytic insight
in short timeframes
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change 20 without notice.
HP Vertica Flex Zone Solution
• Slash development time by
eliminating schema creation
• Explore data with existing BI/
visualization tools for
maximum insight
• Operationalize data in one
single step for fast analytics
• Focus team on data analysis
(not wrestling with data
formats)

21. Thank you!
Jeff Healey
jeff.a.healey@hp.com
617-386-4591
Eamon O’Neill
eamon@hp.com
617-386 4604
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.

22. Twitter Tag: #briefr
The Briefing Room
Perceptions & Questions
Analyst:
Robin Bloor

23. The Data Reservoir
Robin Bloor, Ph.D.

24. Hadoop as the Data Reservoir

25. Big Data and the Data Reservoir

26. The Workload Paradigm Shift
u Previously, we viewed
database workloads as
an i/o optimization
problem
u With analytics the
workload is a very
variable mix of i/o and
calculation
u No databases were built
precisely for this – not
even Big Data databases

27. A Process, Not an Activity
u Data analytics is a multi-disciplinary
end-to-end
process
u Until recently it was a
walled-garden, but the
walls were torn down by
• Data availability
• Scalable technology
• Open source tools
u Hadoop has a role here

28. The Hadoop Ecosystem
u Even though it may
not seem so, Hadoop
is in its infancy
u Hadoop’s popularity
guarantees its future
u Its future is also
guaranteed by its
commercial
ecosystem

29. u What do you see as the fundamental division of
workload between Hadoop, Flex Zone and
Vertica?
u Which specific components of the Hadoop
ecosystem do you recommend using?
u Do you support JSON? If so, for which contexts
and in what way?

30. u Is there any special optimization in Vertica
between query and analytical workloads.
u Please describe the discovery and definition of
metadata from Hadoop, through Flex Zone and
into Vertica
u Why do you think Hadoop is important from a
technical perspective?

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The Briefing Room

32. This Month: BIG DATA
May: DATABASE
June: ANALYTICS & MACHINE LEARNING
www.insideanalysis.com/webcasts/the-briefing-room
Twitter Tag: #briefr
The Briefing Room
Upcoming Topics
2014 Editorial Calendar at
www.insideanalysis.com

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THANK YOU
for your
ATTENTION!
The Briefing Room