The Briefing Room with Robin Bloor and ParAccel Live Webcast on Feb. 19, 2013 Experienced analysts know there is no single platform that can handle all types of analytic processing efficiently. Invariably, data-driven organizations will use a variety of engines to refine their raw data into usable insights. There are several down sides to this heterogeneity, not the least of which is poor collaboration. But that's starting to change, as many companies focus on creative ways to foster analytical cooperation. Check out the slides from this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why collaboration in the design and use of analytical applications can have wide-ranging impacts on an organization. He'll be briefed by John Santaferraro of ParAccel, who will tout his company's Cooperative Analytic Processing Architecture, designed to perform sophisticated deep analytics on large amounts of data quickly. CAPA can orchestrate the processing power of other engines in its ecosystem, including data warehouses and Hadoop implementations. Visit: http://www.insideanalysis.com

1. The Briefing Room

2. Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
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3. Mission
! 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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4. FEBRUARY: Analytics
March: OPERATIONAL INTELLIGENCE
April: INTELLIGENCE
May: INTEGRATION
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5. Analytics
FROM THIS
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6. Analytics
TO THIS
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7. Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
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8. ParAccel
! The ParAccel Analytic Platform: analytic database,
extensibility framework, on demand integration and
integrated analytics
! The Platform connects to existing infrastructures and
industry standard BI tools
! Last month Gartner included ParAccel in its Magic Quadrant
for Data Warehouse Database Management Systems
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9. John Santaferraro
John Santaferraro is the Vice President of Solutions
and Product Marketing at ParAccel. Prior to joining
ParAccel, Santaferraro was an independent industry
analyst in the business intelligence and analytics
market. Before that he developed and executed a
vertical market strategy for Hewlett Packard's BI
group, focusing on energy, communications, retail,
healthcare and financial services; he was also
instrumental in helping establish HP’s new BI
business group with a combination of solutions,
products and consulting. In 2000, John founded a
marketing and sales consulting company, Ferraro
Consulting, providing business acceleration strategy
for technology companies.
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10. ParAccel
and
Unconstrained
Analy1cs
Coopera1ve
Analy1c
Processing
Takes
Center
Stage
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 10

11. Driving
the
Analy/c
Revolu/on
Big
Data
New
Analy/cs
Corporate
Data
Descrip1ve
Machine
Data
Prescrip1ve
Conversa1onal
Data
Predic1ve
Open
Source
Data
Preventa1ve
New
Analy1c
Requirements
Speed
Sophis1ca1on
Interac1on
Next
Genera/on
Analy/c
Pla8orms
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 11

12. ParAccel
Enables
Coopera/ve
Analy/c
Processing
Business
Intelligence
Advanced
Analy/c
and
Repor/ng
Tools
Analy/cs
Applica/ons
ParAccel
Analy/c
Pla8orm
Enterprise
Hadoop
Data
Warehouse
On
Demand
Integra/on
Embedded
3rd
Party
Big
Data
Machine
Opera/onal
Streaming
Analy/cs
Info
Logs
Apps
Data
Data
Data
Provider
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 12

13. U/lize
the
Most
Dynamic
Analy/c
Interac/on
u Most
extensive
interac1ve
connec1vity
to
other
plaHorms
and
data
ParAccel
ParAccel
ParAccel
Teradata
ODI
ODBC
ODI
Hadoop
ODI
module
module
module
ParAccel
Analy/c
Pla8orm
Enterprise
Hadoop
Data
Warehouse
On
Demand
Integra/on
Services
Embedded
3rd
Party
Big
Data
Machine
Opera/onal
Streaming
Analy/cs
Info
Logs
Apps
Data
Data
Data
Provider
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 13

14. U/lize
the
Most
Dynamic
Analy/c
Interac/on
u Most
versa1le
integra1on
service
layer
for
an
analy1c
plaHorm
1. Share
both
data
and
processes
in
both
direc1ons
2. Transform
incoming
data
for
analy1c
performance
3. Interact
with
many
programming
languages
(Java,
Python,
more)
4. Persist
or
stream
data
through
analy1c
processing
5. Rapidly
build
new
On
Demand
Integra1on
modules
ParAccel
Analy/c
Pla8orm
Enterprise
Hadoop
Data
Warehouse
On
Demand
Integra/on
Services
Embedded
3rd
Party
Big
Data
Machine
Opera/onal
Streaming
Analy/cs
Info
Logs
Apps
Data
Data
Data
Provider
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 14

15. Deliver
Analy/c
Services
for
En/re
Ecosystems
Business
Business
Process
Business
Process
Business
Big
Data
Big
Data
Embedded
Process
Embedded
Process
App
Big
Data
App
Big
Data
Analy/cs
Embedded
Analy/cs
Embedded
App
App
Analy/cs
Analy/cs
Business
Business
Business
Business
Process
Process
Process
Process
Big
Data
Big
Data
Big
Data
Big
Data
Embedded
Embedded
Embedded
Embedded
App
App
App
App
Analy/cs
Analy/cs
Analy/cs
Analy/cs
ParAccel
Analy/c
Pla8orm
Enterprise
Hadoop
Data
Warehouse
3rd
Party
Machine
Opera/onal
Streaming
Data
Info
Logs
Data
Data
Data
Data
Provider
Copyright 2012 ParAccel, Inc. 15

16. ParAccel
Analy/c
Pla8orm
-­‐ Built
for
High
Performance,
Interac/ve
Analy/cs
On
Demand
Integra/on
Integrated
Analy/cs
Database
ParAccel
Analy/c
Pla8orm
Basic
Analy1cs
Teradata
Advanced
Analy1cs
Hadoop
Analy/c
Engine
Streaming
Data
Columnar
Applica1ons
Compression
Compiled
Parallel
Processing
SQL
Op1miza1on
Data
Scale
In-­‐Memory
Op/on
Available
Plan
Op1miza1on
Analy1c
Scale
Execu1on
Op1miza1on
User
Scale
Comms
Op1miza1on
Interac1ve
Scale
I/O
Op1miza1on
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 16

17. Run
the
Highest
Performing
Analy/c
Pla8orm
Parallel
Loading
1TB
per
node,
per
hour,
up
to
160
nodes,
without
complex
data
prepara1on
SQL
Op1miza1on
Extreme
SQL
Support
with
breakdown
into
2000
segments,
MPP
and
data-­‐aware
Planning
Op1miza1on
Choose
the
best
from
billions
of
compe1ng
plans
based
on
cos1ng
model
Execu1on
Op1miza1on
Final
op1miza1on
based
on
resources
available
In-­‐Database
Analy1cs
Store
and
run
SQL,
aggregate,
and
analy1c
func1ons
in
the
database
applica1on
Compiled
Queries
Queries
compiled
to
run
within
the
database
on
each
individual
node
Workload
Management
Establish
query
classes
for
long,
short,
and
interac1ve
queries
Parallel
Processing
Each
node
processes,
pipelines,
and
leverages
both
columnar
&
compression
Communica1on
Op1miza1on
Packet
delivery
op1mized
for
analy1cs,
low
overhead,
plus
Virtual
Hotwire
I/O
Op1miza1on
Intelligent
Prefetch,
Intelligent
Caching
of
Data
In-­‐Memory
In-­‐Memory
Op1on:
Lock
all
data
and
processes
to
run
in-­‐memory

18. Total
Customer
Value
-­‐
Time
to
Value
Oracle Report Building
Model Load Build Test Tune Query
20 hours 2 hours 3 hours 6 hours 8 hours 2 hours
Total = 41 hours
ParAccel
X
Model Load X
Build X
Test X
Tune Query
45 seconds 15 seconds
Total = 1 minute
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 18

19. Total
Customer
Value
-­‐
Time
to
Value
Oracle Shrink Processing
Model Load Build Test Tune Query
20 hours 2 hours 3 hours 6 hours 8 hours 46 hours
Total = 85 hours
ParAccel
X
Model Load X
Build X
Test X
Tune Query
45 seconds 30 seconds
Total = 1 minute, 15 seconds
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 19

20. Deliver
Unconstrained
Analy/cs
Unconstrained Analytics
Load and Go
Run Ad Hoc Queries
ParAccel Analytic Platform Query Any Time
Query Any Data
Query All Data
Run Any Analytics
Execute Sophisticated Analytics
Return Results Quickly
Iterate Quickly Through Discovery
Share Workloads With Any Platform
Support All Analysts
Run Many Applications
Create Analytic Services
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 20

21. Envisioning
Unconstrained
Analy/cs
What
are
the
immediate,
pending,
and
“no
constraints”
opportuni1es
for
analy/cs?
Immediate Needs
Pending Needs
Weekly Market
Basket Analysis No Constraints
Daily Market
Basket Analysis On Demand
Market Basket
Analysis
Demand signaling
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 21

22. Envisioning
Unconstrained
Analy/cs
What
are
the
immediate,
pending,
and
“no
constraints”
opportuni1es
for
data
expansion?
Immediate Needs
Pending Needs
Point of Sale +
Loyalty + Credit + No Constraints
Partner Data
Pyschographic
6 Years Data Social Media Data
2 Years Data Archived,
Accessible
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 22

23. Envisioning
Unconstrained
Analy/cs
What
are
the
immediate,
pending,
and
“no
constraints”
opportuni1es
for
analyst
communi/es?
Immediate Needs
Pending Needs
Business Analysts
No Constraints
Store Managers
Suppliers
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 23

24. Increasing
Analyst
Produc/vity
&
Innova/on
Before ParAccel With ParAccel
Productivity
Copyright 2013 ParAccel, Inc. Confidential. Do not Distribute. 24

25. Ques1ons
and
Answers
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26. Perceptions & Questions
Analyst:
Robin Bloor
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27. The Bloor Group

28. A Schism in BI
BI IS FRAGMENTING BETWEEN TRADITIONAL BI AND ANALYTICS
Hindsight & Oversight Insight & Foresight
Relatively easy to Modeling,
accommodate interactive &
technically conversational,
highly variable
The Bloor Group

29. Big Data is New Data (Mostly)
Machine generated data (logs)
Web data
Social media data
Public data services
Supply chain data
Real-time data flows
MOST OF THE VALUE IS IN
The Bloor Group

30. The Data Analytics Issue
The Bloor Group

31. Conversational Analytics
The Bloor Group

32. Boiling It Down
The data analyst needs to be able to
MARSHAL the data
It is all about TIME TO INSIGHT – as
long as that is followed by action
The Bloor Group

33. ! In my view we have reached a situation where
there will be multiple “data engines.” Is that
ParAccel’s view?
! Data analytics is usually 50% data prep
(merging, cleansing, joining, transformation,
etc.). How does ParAccel accommodate that?
! There are many analytics approaches and
algorithms. What is the breadth of ParAccel’s
capability?
! How does it accommodate algorithmic packages?
The R Language?
The Bloor Group

34. ! In your view, is the “age of the data
warehouse” over?
! What is ParAccel’s attitude to the cloud, or
more specifically where would ParAccel
recommend cloud deployment?
! Which sectors/businesses are currently in
ParAccel’s “sweet spot”?
! Which companies/products do you regard as
competitors/partners?
The Bloor Group

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36. Upcoming Topics
This month: Analytics
March: Operational
Intelligence
April: Intelligence
May: Integration
www.insideanalysis.com
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37. Thank You
for Your
Attention
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